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2503.19102 | Debdipta Goswami | Shahab Ataei, Dipankar Maity, and Debdipta Goswami | QSID-MPC: Model Predictive Control with System Identification from
Quantized Data | 6 pages, 2 figures | null | null | null | eess.SY cs.SY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Least-square system identification is widely used for data-driven
model-predictive control (MPC) of unknown or partially known systems. This
letter investigates how the system identification and subsequent MPC is
affected when the state and input data is quantized. Specifically, we examine
the fundamental connection between model error and quantization resolution and
how that affects the stability and boundedness of the MPC tracking error.
Furthermore, we demonstrate that, with a sufficiently rich dataset, the model
error is bounded by a function of quantization resolution and the MPC tracking
error is also ultimately bounded similarly. The theory is validated through
numerical experiments conducted on two different linear dynamical systems.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 19:39:25 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Ataei",
"Shahab",
""
],
[
"Maity",
"Dipankar",
""
],
[
"Goswami",
"Debdipta",
""
]
] | TITLE: QSID-MPC: Model Predictive Control with System Identification from
Quantized Data
ABSTRACT: Least-square system identification is widely used for data-driven
model-predictive control (MPC) of unknown or partially known systems. This
letter investigates how the system identification and subsequent MPC is
affected when the state and input data is quantized. Specifically, we examine
the fundamental connection between model error and quantization resolution and
how that affects the stability and boundedness of the MPC tracking error.
Furthermore, we demonstrate that, with a sufficiently rich dataset, the model
error is bounded by a function of quantization resolution and the MPC tracking
error is also ultimately bounded similarly. The theory is validated through
numerical experiments conducted on two different linear dynamical systems.
| no_new_dataset | 0.947478 |
2503.19115 | Jiaxin Jin | Amey Choudhary, Jiaxin Jin, Abhishek Deshpande | Implementation of Support Vector Machines using Reaction Networks | 26 pages, 4 figures | null | null | null | q-bio.MN cs.NE | http://creativecommons.org/licenses/by/4.0/ | Can machine learning algorithms be implemented using chemical reaction
networks? We demonstrate that this is possible in the case of support vector
machines (SVMs). SVMs are powerful tools for data classification, leveraging VC
theory to handle high-dimensional data and small datasets effectively. In this
work, we propose a reaction network scheme for implementing SVMs, utilizing the
steady-state behavior of reaction network dynamics to model key computational
aspects of SVMs. This approach introduces a novel biochemical framework for
implementing machine learning algorithms in non-traditional computational
environments.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 20:09:14 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Choudhary",
"Amey",
""
],
[
"Jin",
"Jiaxin",
""
],
[
"Deshpande",
"Abhishek",
""
]
] | TITLE: Implementation of Support Vector Machines using Reaction Networks
ABSTRACT: Can machine learning algorithms be implemented using chemical reaction
networks? We demonstrate that this is possible in the case of support vector
machines (SVMs). SVMs are powerful tools for data classification, leveraging VC
theory to handle high-dimensional data and small datasets effectively. In this
work, we propose a reaction network scheme for implementing SVMs, utilizing the
steady-state behavior of reaction network dynamics to model key computational
aspects of SVMs. This approach introduces a novel biochemical framework for
implementing machine learning algorithms in non-traditional computational
environments.
| no_new_dataset | 0.951953 |
2503.19119 | Matteo Maspero | Yiling Wang, Elia Lombardo, Adrian Thummerer, Tom Bl\"ocker, Yu Fan,
Yue Zhao, Christianna Iris Papadopoulou, Coen Hurkmans, Rob H.N. Tijssen, Pia
A.W. G\"orts, Shyama U. Tetar, Davide Cusumano, Martijn P.W. Intven, Pim
Borman, Marco Riboldi, Denis Dud\'a\v{s}, Hilary Byrne, Lorenzo Placidi,
Marco Fusella, Michael Jameson, Miguel Palacios, Paul Cobussen, Tobias
Finazzi, Cornelis J.A. Haasbeek, Paul Keall, Christopher Kurz, Guillaume
Landry and Matteo Maspero | TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided
radiotherapy | 10 pages, 5 figures, 2 tables; submitted to Medical Physics | null | null | null | physics.med-ph cs.CV | http://creativecommons.org/licenses/by/4.0/ | Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is
becoming increasingly important when treating cancer patients with
radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time
motion management during irradiation. This paper presents a multi-institutional
real-time MRI time series dataset from different MRI-linac vendors. The dataset
is designed to support developing and evaluating real-time tumor localization
(tracking) algorithms for MRI-guided radiotherapy within the TrackRAD2025
challenge (https://trackrad2025.grand-challenge.org/).
Acquisition and validation methods: The dataset consists of sagittal 2D cine
MRIs in 585 patients from six centers (3 Dutch, 1 German, 1 Australian, and 1
Chinese). Tumors in the thorax, abdomen, and pelvis acquired on two
commercially available MRI-linacs (0.35 T and 1.5 T) were included. For 108
cases, irradiation targets or tracking surrogates were manually segmented on
each temporal frame. The dataset was randomly split into a public training set
of 527 cases (477 unlabeled and 50 labeled) and a private testing set of 58
cases (all labeled).
Data Format and Usage Notes: The data is publicly available under the
TrackRAD2025 collection: https://doi.org/10.57967/hf/4539. Both the images and
segmentations for each patient are available in metadata format.
Potential Applications: This novel clinical dataset will enable the
development and evaluation of real-time tumor localization algorithms for
MRI-guided radiotherapy. By enabling more accurate motion management and
adaptive treatment strategies, this dataset has the potential to advance the
field of radiotherapy significantly.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 20:14:42 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Wang",
"Yiling",
""
],
[
"Lombardo",
"Elia",
""
],
[
"Thummerer",
"Adrian",
""
],
[
"Blöcker",
"Tom",
""
],
[
"Fan",
"Yu",
""
],
[
"Zhao",
"Yue",
""
],
[
"Papadopoulou",
"Christianna Iris",
""
],
[
"Hurkmans",
"Coen",
""
],
[
"Tijssen",
"Rob H. N.",
""
],
[
"Görts",
"Pia A. W.",
""
],
[
"Tetar",
"Shyama U.",
""
],
[
"Cusumano",
"Davide",
""
],
[
"Intven",
"Martijn P. W.",
""
],
[
"Borman",
"Pim",
""
],
[
"Riboldi",
"Marco",
""
],
[
"Dudáš",
"Denis",
""
],
[
"Byrne",
"Hilary",
""
],
[
"Placidi",
"Lorenzo",
""
],
[
"Fusella",
"Marco",
""
],
[
"Jameson",
"Michael",
""
],
[
"Palacios",
"Miguel",
""
],
[
"Cobussen",
"Paul",
""
],
[
"Finazzi",
"Tobias",
""
],
[
"Haasbeek",
"Cornelis J. A.",
""
],
[
"Keall",
"Paul",
""
],
[
"Kurz",
"Christopher",
""
],
[
"Landry",
"Guillaume",
""
],
[
"Maspero",
"Matteo",
""
]
] | TITLE: TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided
radiotherapy
ABSTRACT: Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is
becoming increasingly important when treating cancer patients with
radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time
motion management during irradiation. This paper presents a multi-institutional
real-time MRI time series dataset from different MRI-linac vendors. The dataset
is designed to support developing and evaluating real-time tumor localization
(tracking) algorithms for MRI-guided radiotherapy within the TrackRAD2025
challenge (https://trackrad2025.grand-challenge.org/).
Acquisition and validation methods: The dataset consists of sagittal 2D cine
MRIs in 585 patients from six centers (3 Dutch, 1 German, 1 Australian, and 1
Chinese). Tumors in the thorax, abdomen, and pelvis acquired on two
commercially available MRI-linacs (0.35 T and 1.5 T) were included. For 108
cases, irradiation targets or tracking surrogates were manually segmented on
each temporal frame. The dataset was randomly split into a public training set
of 527 cases (477 unlabeled and 50 labeled) and a private testing set of 58
cases (all labeled).
Data Format and Usage Notes: The data is publicly available under the
TrackRAD2025 collection: https://doi.org/10.57967/hf/4539. Both the images and
segmentations for each patient are available in metadata format.
Potential Applications: This novel clinical dataset will enable the
development and evaluation of real-time tumor localization algorithms for
MRI-guided radiotherapy. By enabling more accurate motion management and
adaptive treatment strategies, this dataset has the potential to advance the
field of radiotherapy significantly.
| new_dataset | 0.956877 |
2503.19134 | Wenhao You | Wenhao You, Bryan Hooi, Yiwei Wang, Youke Wang, Zong Ke, Ming-Hsuan
Yang, Zi Huang, Yujun Cai | MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for
Red-Team Jailbreak Attacks | null | null | null | null | cs.CL cs.CR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | While safety mechanisms have significantly progressed in filtering harmful
text inputs, MLLMs remain vulnerable to multimodal jailbreaks that exploit
their cross-modal reasoning capabilities. We present MIRAGE, a novel multimodal
jailbreak framework that exploits narrative-driven context and role immersion
to circumvent safety mechanisms in Multimodal Large Language Models (MLLMs). By
systematically decomposing the toxic query into environment, role, and action
triplets, MIRAGE constructs a multi-turn visual storytelling sequence of images
and text using Stable Diffusion, guiding the target model through an engaging
detective narrative. This process progressively lowers the model's defences and
subtly guides its reasoning through structured contextual cues, ultimately
eliciting harmful responses. In extensive experiments on the selected datasets
with six mainstream MLLMs, MIRAGE achieves state-of-the-art performance,
improving attack success rates by up to 17.5% over the best baselines.
Moreover, we demonstrate that role immersion and structured semantic
reconstruction can activate inherent model biases, facilitating the model's
spontaneous violation of ethical safeguards. These results highlight critical
weaknesses in current multimodal safety mechanisms and underscore the urgent
need for more robust defences against cross-modal threats.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 20:38:42 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"You",
"Wenhao",
""
],
[
"Hooi",
"Bryan",
""
],
[
"Wang",
"Yiwei",
""
],
[
"Wang",
"Youke",
""
],
[
"Ke",
"Zong",
""
],
[
"Yang",
"Ming-Hsuan",
""
],
[
"Huang",
"Zi",
""
],
[
"Cai",
"Yujun",
""
]
] | TITLE: MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for
Red-Team Jailbreak Attacks
ABSTRACT: While safety mechanisms have significantly progressed in filtering harmful
text inputs, MLLMs remain vulnerable to multimodal jailbreaks that exploit
their cross-modal reasoning capabilities. We present MIRAGE, a novel multimodal
jailbreak framework that exploits narrative-driven context and role immersion
to circumvent safety mechanisms in Multimodal Large Language Models (MLLMs). By
systematically decomposing the toxic query into environment, role, and action
triplets, MIRAGE constructs a multi-turn visual storytelling sequence of images
and text using Stable Diffusion, guiding the target model through an engaging
detective narrative. This process progressively lowers the model's defences and
subtly guides its reasoning through structured contextual cues, ultimately
eliciting harmful responses. In extensive experiments on the selected datasets
with six mainstream MLLMs, MIRAGE achieves state-of-the-art performance,
improving attack success rates by up to 17.5% over the best baselines.
Moreover, we demonstrate that role immersion and structured semantic
reconstruction can activate inherent model biases, facilitating the model's
spontaneous violation of ethical safeguards. These results highlight critical
weaknesses in current multimodal safety mechanisms and underscore the urgent
need for more robust defences against cross-modal threats.
| no_new_dataset | 0.944074 |
2503.19145 | Marco Garosi | Marco Garosi, Alessandro Conti, Gaowen Liu, Elisa Ricci, Massimiliano
Mancini | Compositional Caching for Training-free Open-vocabulary Attribute
Detection | CVPR 2025. Project website at https://comca-attributes.github.io/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Attribute detection is crucial for many computer vision tasks, as it enables
systems to describe properties such as color, texture, and material. Current
approaches often rely on labor-intensive annotation processes which are
inherently limited: objects can be described at an arbitrary level of detail
(e.g., color vs. color shades), leading to ambiguities when the annotators are
not instructed carefully. Furthermore, they operate within a predefined set of
attributes, reducing scalability and adaptability to unforeseen downstream
applications. We present Compositional Caching (ComCa), a training-free method
for open-vocabulary attribute detection that overcomes these constraints. ComCa
requires only the list of target attributes and objects as input, using them to
populate an auxiliary cache of images by leveraging web-scale databases and
Large Language Models to determine attribute-object compatibility. To account
for the compositional nature of attributes, cache images receive soft attribute
labels. Those are aggregated at inference time based on the similarity between
the input and cache images, refining the predictions of underlying
Vision-Language Models (VLMs). Importantly, our approach is model-agnostic,
compatible with various VLMs. Experiments on public datasets demonstrate that
ComCa significantly outperforms zero-shot and cache-based baselines, competing
with recent training-based methods, proving that a carefully designed
training-free approach can successfully address open-vocabulary attribute
detection.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 21:00:37 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Garosi",
"Marco",
""
],
[
"Conti",
"Alessandro",
""
],
[
"Liu",
"Gaowen",
""
],
[
"Ricci",
"Elisa",
""
],
[
"Mancini",
"Massimiliano",
""
]
] | TITLE: Compositional Caching for Training-free Open-vocabulary Attribute
Detection
ABSTRACT: Attribute detection is crucial for many computer vision tasks, as it enables
systems to describe properties such as color, texture, and material. Current
approaches often rely on labor-intensive annotation processes which are
inherently limited: objects can be described at an arbitrary level of detail
(e.g., color vs. color shades), leading to ambiguities when the annotators are
not instructed carefully. Furthermore, they operate within a predefined set of
attributes, reducing scalability and adaptability to unforeseen downstream
applications. We present Compositional Caching (ComCa), a training-free method
for open-vocabulary attribute detection that overcomes these constraints. ComCa
requires only the list of target attributes and objects as input, using them to
populate an auxiliary cache of images by leveraging web-scale databases and
Large Language Models to determine attribute-object compatibility. To account
for the compositional nature of attributes, cache images receive soft attribute
labels. Those are aggregated at inference time based on the similarity between
the input and cache images, refining the predictions of underlying
Vision-Language Models (VLMs). Importantly, our approach is model-agnostic,
compatible with various VLMs. Experiments on public datasets demonstrate that
ComCa significantly outperforms zero-shot and cache-based baselines, competing
with recent training-based methods, proving that a carefully designed
training-free approach can successfully address open-vocabulary attribute
detection.
| no_new_dataset | 0.948632 |
2503.19146 | Yorick Estievenart | Yorick Estievenart, Sukanya Patra, Souhaib Ben Taieb | Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated
Solar Power Plants | null | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Efficient and reliable operation of Concentrated Solar Power (CSP) plants is
essential for meeting the growing demand for sustainable energy. However,
high-temperature solar receivers face severe operational risks, such as
freezing, deformation, and corrosion, resulting in costly downtime and
maintenance. To monitor CSP plants, cameras mounted on solar receivers record
infrared images at irregular intervals ranging from one to five minutes
throughout the day. Anomalous images can be detected by thresholding an anomaly
score, where the threshold is chosen to optimize metrics such as the F1-score
on a validation set. This work proposes a framework for generating more
reliable decision thresholds with finite-sample coverage guarantees on any
chosen risk function. Our framework also incorporates an abstention mechanism,
allowing high-risk predictions to be deferred to domain experts. Second, we
propose a density forecasting method to estimate the likelihood of an observed
image given a sequence of previously observed images, using this likelihood as
its anomaly score. Third, we analyze the deployment results of our framework
across multiple training scenarios over several months for two CSP plants. This
analysis provides valuable insights to our industry partner for optimizing
maintenance operations. Finally, given the confidential nature of our dataset,
we provide an extended simulated dataset, leveraging recent advancements in
generative modeling to create diverse thermal images that simulate multiple CSP
plants. Our code is publicly available.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 21:02:20 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Estievenart",
"Yorick",
""
],
[
"Patra",
"Sukanya",
""
],
[
"Taieb",
"Souhaib Ben",
""
]
] | TITLE: Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated
Solar Power Plants
ABSTRACT: Efficient and reliable operation of Concentrated Solar Power (CSP) plants is
essential for meeting the growing demand for sustainable energy. However,
high-temperature solar receivers face severe operational risks, such as
freezing, deformation, and corrosion, resulting in costly downtime and
maintenance. To monitor CSP plants, cameras mounted on solar receivers record
infrared images at irregular intervals ranging from one to five minutes
throughout the day. Anomalous images can be detected by thresholding an anomaly
score, where the threshold is chosen to optimize metrics such as the F1-score
on a validation set. This work proposes a framework for generating more
reliable decision thresholds with finite-sample coverage guarantees on any
chosen risk function. Our framework also incorporates an abstention mechanism,
allowing high-risk predictions to be deferred to domain experts. Second, we
propose a density forecasting method to estimate the likelihood of an observed
image given a sequence of previously observed images, using this likelihood as
its anomaly score. Third, we analyze the deployment results of our framework
across multiple training scenarios over several months for two CSP plants. This
analysis provides valuable insights to our industry partner for optimizing
maintenance operations. Finally, given the confidential nature of our dataset,
we provide an extended simulated dataset, leveraging recent advancements in
generative modeling to create diverse thermal images that simulate multiple CSP
plants. Our code is publicly available.
| new_dataset | 0.963643 |
2503.19149 | Christian Hurry | Christian John Hurry, Jinjie Zhang, Olubukola Ishola, Emma Slade,
Cuong Q. Nguyen | Out-of-distribution evaluations of channel agnostic masked autoencoders
in fluorescence microscopy | 13 pages, 5 figures | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Developing computer vision for high-content screening is challenging due to
various sources of distribution-shift caused by changes in experimental
conditions, perturbagens, and fluorescent markers. The impact of different
sources of distribution-shift are confounded in typical evaluations of models
based on transfer learning, which limits interpretations of how changes to
model design and training affect generalisation. We propose an evaluation
scheme that isolates sources of distribution-shift using the JUMP-CP dataset,
allowing researchers to evaluate generalisation with respect to specific
sources of distribution-shift. We then present a channel-agnostic masked
autoencoder $\mathbf{Campfire}$ which, via a shared decoder for all channels,
scales effectively to datasets containing many different fluorescent markers,
and show that it generalises to out-of-distribution experimental batches,
perturbagens, and fluorescent markers, and also demonstrates successful
transfer learning from one cell type to another.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 21:07:58 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Hurry",
"Christian John",
""
],
[
"Zhang",
"Jinjie",
""
],
[
"Ishola",
"Olubukola",
""
],
[
"Slade",
"Emma",
""
],
[
"Nguyen",
"Cuong Q.",
""
]
] | TITLE: Out-of-distribution evaluations of channel agnostic masked autoencoders
in fluorescence microscopy
ABSTRACT: Developing computer vision for high-content screening is challenging due to
various sources of distribution-shift caused by changes in experimental
conditions, perturbagens, and fluorescent markers. The impact of different
sources of distribution-shift are confounded in typical evaluations of models
based on transfer learning, which limits interpretations of how changes to
model design and training affect generalisation. We propose an evaluation
scheme that isolates sources of distribution-shift using the JUMP-CP dataset,
allowing researchers to evaluate generalisation with respect to specific
sources of distribution-shift. We then present a channel-agnostic masked
autoencoder $\mathbf{Campfire}$ which, via a shared decoder for all channels,
scales effectively to datasets containing many different fluorescent markers,
and show that it generalises to out-of-distribution experimental batches,
perturbagens, and fluorescent markers, and also demonstrates successful
transfer learning from one cell type to another.
| no_new_dataset | 0.943919 |
2503.19152 | Shoffan Saifullah | Shoffan Saifullah and Rafa{\l} Dre\.zewski | PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise
Multimodal Brain Tumor Segmentation | 9 pages, 6 figures, 4 tables, Gecco 2025 Conference | null | null | null | eess.IV cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | Medical image segmentation, particularly for brain tumor analysis, demands
precise and computationally efficient models due to the complexity of
multimodal MRI datasets and diverse tumor morphologies. This study introduces
PSO-UNet, which integrates Particle Swarm Optimization (PSO) with the U-Net
architecture for dynamic hyperparameter optimization. Unlike traditional manual
tuning or alternative optimization approaches, PSO effectively navigates
complex hyperparameter search spaces, explicitly optimizing the number of
filters, kernel size, and learning rate. PSO-UNet substantially enhances
segmentation performance, achieving Dice Similarity Coefficients (DSC) of
0.9578 and 0.9523 and Intersection over Union (IoU) scores of 0.9194 and 0.9097
on the BraTS 2021 and Figshare datasets, respectively. Moreover, the method
reduces computational complexity significantly, utilizing only 7.8 million
parameters and executing in approximately 906 seconds, markedly faster than
comparable U-Net-based frameworks. These outcomes underscore PSO-UNet's robust
generalization capabilities across diverse MRI modalities and tumor
classifications, emphasizing its clinical potential and clear advantages over
conventional hyperparameter tuning methods. Future research will explore hybrid
optimization strategies and validate the framework against other bio-inspired
algorithms to enhance its robustness and scalability.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 21:14:08 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Saifullah",
"Shoffan",
""
],
[
"Dreżewski",
"Rafał",
""
]
] | TITLE: PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise
Multimodal Brain Tumor Segmentation
ABSTRACT: Medical image segmentation, particularly for brain tumor analysis, demands
precise and computationally efficient models due to the complexity of
multimodal MRI datasets and diverse tumor morphologies. This study introduces
PSO-UNet, which integrates Particle Swarm Optimization (PSO) with the U-Net
architecture for dynamic hyperparameter optimization. Unlike traditional manual
tuning or alternative optimization approaches, PSO effectively navigates
complex hyperparameter search spaces, explicitly optimizing the number of
filters, kernel size, and learning rate. PSO-UNet substantially enhances
segmentation performance, achieving Dice Similarity Coefficients (DSC) of
0.9578 and 0.9523 and Intersection over Union (IoU) scores of 0.9194 and 0.9097
on the BraTS 2021 and Figshare datasets, respectively. Moreover, the method
reduces computational complexity significantly, utilizing only 7.8 million
parameters and executing in approximately 906 seconds, markedly faster than
comparable U-Net-based frameworks. These outcomes underscore PSO-UNet's robust
generalization capabilities across diverse MRI modalities and tumor
classifications, emphasizing its clinical potential and clear advantages over
conventional hyperparameter tuning methods. Future research will explore hybrid
optimization strategies and validate the framework against other bio-inspired
algorithms to enhance its robustness and scalability.
| no_new_dataset | 0.944587 |
2503.19161 | Jakob Abe{\ss}er | Jakob Abe{\ss}er and Simon Schw\"ar and Meinard M\"uller | Pitch Contour Exploration Across Audio Domains: A Vision-Based Transfer
Learning Approach | null | null | null | null | eess.AS cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study examines pitch contours as a unifying semantic construct prevalent
across various audio domains including music, speech, bioacoustics, and
everyday sounds. Analyzing pitch contours offers insights into the universal
role of pitch in the perceptual processing of audio signals and contributes to
a deeper understanding of auditory mechanisms in both humans and animals.
Conventional pitch-tracking methods, while optimized for music and speech, face
challenges in handling much broader frequency ranges and more rapid pitch
variations found in other audio domains. This study introduces a vision-based
approach to pitch contour analysis that eliminates the need for explicit
pitch-tracking. The approach uses a convolutional neural network, pre-trained
for object detection in natural images and fine-tuned with a dataset of
synthetically generated pitch contours, to extract key contour parameters from
the time-frequency representation of short audio segments. A diverse set of
eight downstream tasks from four audio domains were selected to provide a
challenging evaluation scenario for cross-domain pitch contour analysis. The
results show that the proposed method consistently surpasses traditional
techniques based on pitch-tracking on a wide range of tasks. This suggests that
the vision-based approach establishes a foundation for comparative studies of
pitch contour characteristics across diverse audio domains.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 21:33:13 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Abeßer",
"Jakob",
""
],
[
"Schwär",
"Simon",
""
],
[
"Müller",
"Meinard",
""
]
] | TITLE: Pitch Contour Exploration Across Audio Domains: A Vision-Based Transfer
Learning Approach
ABSTRACT: This study examines pitch contours as a unifying semantic construct prevalent
across various audio domains including music, speech, bioacoustics, and
everyday sounds. Analyzing pitch contours offers insights into the universal
role of pitch in the perceptual processing of audio signals and contributes to
a deeper understanding of auditory mechanisms in both humans and animals.
Conventional pitch-tracking methods, while optimized for music and speech, face
challenges in handling much broader frequency ranges and more rapid pitch
variations found in other audio domains. This study introduces a vision-based
approach to pitch contour analysis that eliminates the need for explicit
pitch-tracking. The approach uses a convolutional neural network, pre-trained
for object detection in natural images and fine-tuned with a dataset of
synthetically generated pitch contours, to extract key contour parameters from
the time-frequency representation of short audio segments. A diverse set of
eight downstream tasks from four audio domains were selected to provide a
challenging evaluation scenario for cross-domain pitch contour analysis. The
results show that the proposed method consistently surpasses traditional
techniques based on pitch-tracking on a wide range of tasks. This suggests that
the vision-based approach establishes a foundation for comparative studies of
pitch contour characteristics across diverse audio domains.
| no_new_dataset | 0.880129 |
2503.19172 | Francesco Cesa | Francesco Cesa, Hannes Bernien and Hannes Pichler | Fast and Error-Correctable Quantum RAM | null | null | null | null | quant-ph physics.atom-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantum devices can process data in a fundamentally different way than
classical computers. To leverage this potential, many algorithms require the
aid of a quantum Random Access Memory (QRAM), i.e. a module capable of
efficiently loading datasets (both classical and quantum) onto the quantum
processor. However, a realization of this fundamental building block is still
outstanding, since existing proposals require prohibitively many resources for
reliable implementations, or are not compatible with current architectures.
Moreover, present approaches cannot be scaled-up, as they do not allow for
efficient quantum error-correction. Here we develop a QRAM design, that enables
fast and robust QRAM calls, naturally allows for fault-tolerant and
error-corrected operation, and can be integrated on present hardware. Our
proposal employs a special quantum resource state that is consumed during the
QRAM call: we discuss how it can be assembled and processed efficiently in a
dedicated module, and give detailed blueprints for modern neutral-atom
processors. Our work places a long missing, fundamental component of quantum
computers within reach of currently available technology; this opens the door
to algorithms featuring practical quantum advantage, including search or
oracular problems, quantum chemistry and machine learning.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 21:51:49 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Cesa",
"Francesco",
""
],
[
"Bernien",
"Hannes",
""
],
[
"Pichler",
"Hannes",
""
]
] | TITLE: Fast and Error-Correctable Quantum RAM
ABSTRACT: Quantum devices can process data in a fundamentally different way than
classical computers. To leverage this potential, many algorithms require the
aid of a quantum Random Access Memory (QRAM), i.e. a module capable of
efficiently loading datasets (both classical and quantum) onto the quantum
processor. However, a realization of this fundamental building block is still
outstanding, since existing proposals require prohibitively many resources for
reliable implementations, or are not compatible with current architectures.
Moreover, present approaches cannot be scaled-up, as they do not allow for
efficient quantum error-correction. Here we develop a QRAM design, that enables
fast and robust QRAM calls, naturally allows for fault-tolerant and
error-corrected operation, and can be integrated on present hardware. Our
proposal employs a special quantum resource state that is consumed during the
QRAM call: we discuss how it can be assembled and processed efficiently in a
dedicated module, and give detailed blueprints for modern neutral-atom
processors. Our work places a long missing, fundamental component of quantum
computers within reach of currently available technology; this opens the door
to algorithms featuring practical quantum advantage, including search or
oracular problems, quantum chemistry and machine learning.
| no_new_dataset | 0.936981 |
2503.19199 | Francis Engelmann | Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang, Ruida
Zhang, Xiangyang Ji, Marc Pollefeys, Francis Engelmann | Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces | Accepted at CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We introduce the task of predicting functional 3D scene graphs for real-world
indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs
that focus on spatial relationships of objects, functional 3D scene graphs
capture objects, interactive elements, and their functional relationships. Due
to the lack of training data, we leverage foundation models, including visual
language models (VLMs) and large language models (LLMs), to encode functional
knowledge. We evaluate our approach on an extended SceneFun3D dataset and a
newly collected dataset, FunGraph3D, both annotated with functional 3D scene
graphs. Our method significantly outperforms adapted baselines, including
Open3DSG and ConceptGraph, demonstrating its effectiveness in modeling complex
scene functionalities. We also demonstrate downstream applications such as 3D
question answering and robotic manipulation using functional 3D scene graphs.
See our project page at https://openfungraph.github.io
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 22:53:19 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhang",
"Chenyangguang",
""
],
[
"Delitzas",
"Alexandros",
""
],
[
"Wang",
"Fangjinhua",
""
],
[
"Zhang",
"Ruida",
""
],
[
"Ji",
"Xiangyang",
""
],
[
"Pollefeys",
"Marc",
""
],
[
"Engelmann",
"Francis",
""
]
] | TITLE: Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces
ABSTRACT: We introduce the task of predicting functional 3D scene graphs for real-world
indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs
that focus on spatial relationships of objects, functional 3D scene graphs
capture objects, interactive elements, and their functional relationships. Due
to the lack of training data, we leverage foundation models, including visual
language models (VLMs) and large language models (LLMs), to encode functional
knowledge. We evaluate our approach on an extended SceneFun3D dataset and a
newly collected dataset, FunGraph3D, both annotated with functional 3D scene
graphs. Our method significantly outperforms adapted baselines, including
Open3DSG and ConceptGraph, demonstrating its effectiveness in modeling complex
scene functionalities. We also demonstrate downstream applications such as 3D
question answering and robotic manipulation using functional 3D scene graphs.
See our project page at https://openfungraph.github.io
| new_dataset | 0.956877 |
2503.19201 | Renpu Liu | Renpu Liu, Peng Wang, Donghao Li, Cong Shen, Jing Yang | A Shared Low-Rank Adaptation Approach to Personalized RLHF | Published as a conference paper at AISTATS 2025 | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal
technique for aligning artificial intelligence systems with human values,
achieving remarkable success in fine-tuning large language models. However,
existing RLHF frameworks often assume that human preferences are relatively
homogeneous and can be captured by a single, unified reward model. This
assumption overlooks the inherent diversity and heterogeneity across
individuals, limiting the adaptability of RLHF to personalized scenarios and
risking misalignments that can diminish user satisfaction and trust in AI
systems. In this paper, we address these challenges by introducing Low-Rank
Adaptation (LoRA) into the personalized RLHF framework. We apply LoRA in the
the aggregated parameter space of all personalized reward functions, thereby
enabling efficient learning of personalized reward models from potentially
limited local datasets. Our approach exploits potential shared structures among
the local ground-truth reward models while allowing for individual adaptation,
without relying on restrictive assumptions about shared representations as in
prior works. We further establish sample complexity guarantees for our method.
Theoretical analysis demonstrates the effectiveness of the proposed approach in
capturing both shared and individual-specific structures within heterogeneous
human preferences, addressing the dual challenge of personalization
requirements and practical data constraints. Experimental results on real-world
datasets corroborate the efficiency of our algorithm in the personalized RLHF
setting.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 23:01:08 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Liu",
"Renpu",
""
],
[
"Wang",
"Peng",
""
],
[
"Li",
"Donghao",
""
],
[
"Shen",
"Cong",
""
],
[
"Yang",
"Jing",
""
]
] | TITLE: A Shared Low-Rank Adaptation Approach to Personalized RLHF
ABSTRACT: Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal
technique for aligning artificial intelligence systems with human values,
achieving remarkable success in fine-tuning large language models. However,
existing RLHF frameworks often assume that human preferences are relatively
homogeneous and can be captured by a single, unified reward model. This
assumption overlooks the inherent diversity and heterogeneity across
individuals, limiting the adaptability of RLHF to personalized scenarios and
risking misalignments that can diminish user satisfaction and trust in AI
systems. In this paper, we address these challenges by introducing Low-Rank
Adaptation (LoRA) into the personalized RLHF framework. We apply LoRA in the
the aggregated parameter space of all personalized reward functions, thereby
enabling efficient learning of personalized reward models from potentially
limited local datasets. Our approach exploits potential shared structures among
the local ground-truth reward models while allowing for individual adaptation,
without relying on restrictive assumptions about shared representations as in
prior works. We further establish sample complexity guarantees for our method.
Theoretical analysis demonstrates the effectiveness of the proposed approach in
capturing both shared and individual-specific structures within heterogeneous
human preferences, addressing the dual challenge of personalization
requirements and practical data constraints. Experimental results on real-world
datasets corroborate the efficiency of our algorithm in the personalized RLHF
setting.
| no_new_dataset | 0.9434 |
2503.19202 | Sara Al-Emadi | Sara Al-Emadi, Yin Yang, Ferda Ofli | Benchmarking Object Detectors under Real-World Distribution Shifts in
Satellite Imagery | Accepted at CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Object detectors have achieved remarkable performance in many applications;
however, these deep learning models are typically designed under the i.i.d.
assumption, meaning they are trained and evaluated on data sampled from the
same (source) distribution. In real-world deployment, however, target
distributions often differ from source data, leading to substantial performance
degradation. Domain Generalisation (DG) seeks to bridge this gap by enabling
models to generalise to Out-Of-Distribution (OOD) data without access to target
distributions during training, enhancing robustness to unseen conditions. In
this work, we examine the generalisability and robustness of state-of-the-art
object detectors under real-world distribution shifts, focusing particularly on
spatial domain shifts. Despite the need, a standardised benchmark dataset
specifically designed for assessing object detection under realistic DG
scenarios is currently lacking. To address this, we introduce Real-World
Distribution Shifts (RWDS), a suite of three novel DG benchmarking datasets
that focus on humanitarian and climate change applications. These datasets
enable the investigation of domain shifts across (i) climate zones and (ii)
various disasters and geographic regions. To our knowledge, these are the first
DG benchmarking datasets tailored for object detection in real-world,
high-impact contexts. We aim for these datasets to serve as valuable resources
for evaluating the robustness and generalisation of future object detection
models. Our datasets and code are available at https://github.com/RWGAI/RWDS.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 23:04:06 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Al-Emadi",
"Sara",
""
],
[
"Yang",
"Yin",
""
],
[
"Ofli",
"Ferda",
""
]
] | TITLE: Benchmarking Object Detectors under Real-World Distribution Shifts in
Satellite Imagery
ABSTRACT: Object detectors have achieved remarkable performance in many applications;
however, these deep learning models are typically designed under the i.i.d.
assumption, meaning they are trained and evaluated on data sampled from the
same (source) distribution. In real-world deployment, however, target
distributions often differ from source data, leading to substantial performance
degradation. Domain Generalisation (DG) seeks to bridge this gap by enabling
models to generalise to Out-Of-Distribution (OOD) data without access to target
distributions during training, enhancing robustness to unseen conditions. In
this work, we examine the generalisability and robustness of state-of-the-art
object detectors under real-world distribution shifts, focusing particularly on
spatial domain shifts. Despite the need, a standardised benchmark dataset
specifically designed for assessing object detection under realistic DG
scenarios is currently lacking. To address this, we introduce Real-World
Distribution Shifts (RWDS), a suite of three novel DG benchmarking datasets
that focus on humanitarian and climate change applications. These datasets
enable the investigation of domain shifts across (i) climate zones and (ii)
various disasters and geographic regions. To our knowledge, these are the first
DG benchmarking datasets tailored for object detection in real-world,
high-impact contexts. We aim for these datasets to serve as valuable resources
for evaluating the robustness and generalisation of future object detection
models. Our datasets and code are available at https://github.com/RWGAI/RWDS.
| new_dataset | 0.968201 |
2503.19209 | Shana Moothedath | Tuan Le and Shana Moothedath | Byzantine Resilient Federated Multi-Task Representation Learning | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task
representation learning framework that handles faulty or malicious agents. Our
approach leverages representation learning through a shared neural network
model, where all clients share fixed layers, except for a client-specific final
layer. This structure captures shared features among clients while enabling
individual adaptation, making it a promising approach for leveraging client
data and computational power in heterogeneous federated settings to learn
personalized models. To learn the model, we employ an alternating gradient
descent strategy: each client optimizes its local model, updates its final
layer, and sends estimates of the shared representation to a central server for
aggregation. To defend against Byzantine agents, we employ geometric median
aggregation for robust client-server communication. Our method enables
personalized learning while maintaining resilience in distributed settings. We
implemented the proposed alternating gradient descent algorithm in a federated
testbed built using Amazon Web Services (AWS) platform and compared its
performance with various benchmark algorithms and their variations. Through
extensive experiments using real-world datasets, including CIFAR-10 and
FEMINIST, we demonstrated the effectiveness and robustness of our approach and
its transferability to new unseen clients with limited data, even in the
presence of Byzantine adversaries.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 23:26:28 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Le",
"Tuan",
""
],
[
"Moothedath",
"Shana",
""
]
] | TITLE: Byzantine Resilient Federated Multi-Task Representation Learning
ABSTRACT: In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task
representation learning framework that handles faulty or malicious agents. Our
approach leverages representation learning through a shared neural network
model, where all clients share fixed layers, except for a client-specific final
layer. This structure captures shared features among clients while enabling
individual adaptation, making it a promising approach for leveraging client
data and computational power in heterogeneous federated settings to learn
personalized models. To learn the model, we employ an alternating gradient
descent strategy: each client optimizes its local model, updates its final
layer, and sends estimates of the shared representation to a central server for
aggregation. To defend against Byzantine agents, we employ geometric median
aggregation for robust client-server communication. Our method enables
personalized learning while maintaining resilience in distributed settings. We
implemented the proposed alternating gradient descent algorithm in a federated
testbed built using Amazon Web Services (AWS) platform and compared its
performance with various benchmark algorithms and their variations. Through
extensive experiments using real-world datasets, including CIFAR-10 and
FEMINIST, we demonstrated the effectiveness and robustness of our approach and
its transferability to new unseen clients with limited data, even in the
presence of Byzantine adversaries.
| no_new_dataset | 0.944125 |
2503.19211 | Mahdi Nasser | Mahdi Nasser, Laura Sayyah, Fadi A. Zaraket | Towards Terminology Management Automation for Arabic | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents a method and supporting tools for automation of
terminology management for Arabic. The tools extract lists of parallel
terminology matching terms in foreign languages to their Arabic counterparts
from field specific texts. This has significant implications as it can be used
to improve consistent translation and use of terms in specialized Arabic
academic books, and provides automated aid for enhancing cross lingual text
processing. This automation of terminology management aims to reduce processing
time, and ensure use of consistent and correct terminology. The extraction
takes advantage of naturally occurring term translations. It considers several
candidate phrases of varying lengths that co-occur next to the foreign terms.
Then it computes several similarity metrics, including lexicographic, phonetic,
morphological, and semantic ones to decide the problem. We experiment with
heuristic, machine learning, and ML with post processing approaches. This paper
reports on a novel curated dataset for the task, an existing expert reviewed
industry parallel corpora, and on the performance of the three approaches. The
best approach achieved 94.9% precision and 92.4% recall.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 23:35:00 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Nasser",
"Mahdi",
""
],
[
"Sayyah",
"Laura",
""
],
[
"Zaraket",
"Fadi A.",
""
]
] | TITLE: Towards Terminology Management Automation for Arabic
ABSTRACT: This paper presents a method and supporting tools for automation of
terminology management for Arabic. The tools extract lists of parallel
terminology matching terms in foreign languages to their Arabic counterparts
from field specific texts. This has significant implications as it can be used
to improve consistent translation and use of terms in specialized Arabic
academic books, and provides automated aid for enhancing cross lingual text
processing. This automation of terminology management aims to reduce processing
time, and ensure use of consistent and correct terminology. The extraction
takes advantage of naturally occurring term translations. It considers several
candidate phrases of varying lengths that co-occur next to the foreign terms.
Then it computes several similarity metrics, including lexicographic, phonetic,
morphological, and semantic ones to decide the problem. We experiment with
heuristic, machine learning, and ML with post processing approaches. This paper
reports on a novel curated dataset for the task, an existing expert reviewed
industry parallel corpora, and on the performance of the three approaches. The
best approach achieved 94.9% precision and 92.4% recall.
| new_dataset | 0.953751 |
2503.19215 | Bilal Alsallakh | Bilal Alsallakh and Timothy Wroge and Vivek Miglani and Narine
Kokhlikyan | On Symmetries in Convolutional Weights | Accepted to the ICLR 2025 Workshop on Weight Space Learning (WSL) | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We explore the symmetry of the mean k x k weight kernel in each layer of
various convolutional neural networks. Unlike individual neurons, the mean
kernels in internal layers tend to be symmetric about their centers instead of
favoring specific directions. We investigate why this symmetry emerges in
various datasets and models, and how it is impacted by certain architectural
choices. We show how symmetry correlates with desirable properties such as
shift and flip consistency, and might constitute an inherent inductive bias in
convolutional neural networks.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2025 23:41:37 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Alsallakh",
"Bilal",
""
],
[
"Wroge",
"Timothy",
""
],
[
"Miglani",
"Vivek",
""
],
[
"Kokhlikyan",
"Narine",
""
]
] | TITLE: On Symmetries in Convolutional Weights
ABSTRACT: We explore the symmetry of the mean k x k weight kernel in each layer of
various convolutional neural networks. Unlike individual neurons, the mean
kernels in internal layers tend to be symmetric about their centers instead of
favoring specific directions. We investigate why this symmetry emerges in
various datasets and models, and how it is impacted by certain architectural
choices. We show how symmetry correlates with desirable properties such as
shift and flip consistency, and might constitute an inherent inductive bias in
convolutional neural networks.
| no_new_dataset | 0.957358 |
2503.19223 | Maaz Salman | Najeebullah, Maaz Salman, Zar Nawab Khan Swati | Face Spoofing Detection using Deep Learning | 26 pages, 9 figures,3 tables | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Digital image spoofing has emerged as a significant security threat in
biometric authentication systems, particularly those relying on facial
recognition. This study evaluates the performance of three vision based models,
MobileNetV2, ResNET50, and Vision Transformer, ViT, for spoof detection in
image classification, utilizing a dataset of 150,986 images divided into
training , 140,002, testing, 10,984, and validation ,39,574, sets. Spoof
detection is critical for enhancing the security of image recognition systems,
and this research compares the models effectiveness through accuracy,
precision, recall, and F1 score metrics. Results reveal that MobileNetV2
outperforms other architectures on the test dataset, achieving an accuracy of
91.59%, precision of 91.72%, recall of 91.59%, and F1 score of 91.58%, compared
to ViT 86.54%, 88.28%, 86.54%, and 86.39%, respectively. On the validation
dataset, MobileNetV2, and ViT excel, with MobileNetV2 slightly ahead at 97.17%
accuracy versus ViT 96.36%. MobileNetV2 demonstrates faster convergence during
training and superior generalization to unseen data, despite both models
showing signs of overfitting. These findings highlight MobileNetV2 balanced
performance and robustness, making it the preferred choice for spoof detection
applications where reliability on new data is essential. The study underscores
the importance of model selection in security sensitive contexts and suggests
MobileNetV2 as a practical solution for real world deployment.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 00:09:21 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Najeebullah",
"",
""
],
[
"Salman",
"Maaz",
""
],
[
"Swati",
"Zar Nawab Khan",
""
]
] | TITLE: Face Spoofing Detection using Deep Learning
ABSTRACT: Digital image spoofing has emerged as a significant security threat in
biometric authentication systems, particularly those relying on facial
recognition. This study evaluates the performance of three vision based models,
MobileNetV2, ResNET50, and Vision Transformer, ViT, for spoof detection in
image classification, utilizing a dataset of 150,986 images divided into
training , 140,002, testing, 10,984, and validation ,39,574, sets. Spoof
detection is critical for enhancing the security of image recognition systems,
and this research compares the models effectiveness through accuracy,
precision, recall, and F1 score metrics. Results reveal that MobileNetV2
outperforms other architectures on the test dataset, achieving an accuracy of
91.59%, precision of 91.72%, recall of 91.59%, and F1 score of 91.58%, compared
to ViT 86.54%, 88.28%, 86.54%, and 86.39%, respectively. On the validation
dataset, MobileNetV2, and ViT excel, with MobileNetV2 slightly ahead at 97.17%
accuracy versus ViT 96.36%. MobileNetV2 demonstrates faster convergence during
training and superior generalization to unseen data, despite both models
showing signs of overfitting. These findings highlight MobileNetV2 balanced
performance and robustness, making it the preferred choice for spoof detection
applications where reliability on new data is essential. The study underscores
the importance of model selection in security sensitive contexts and suggests
MobileNetV2 as a practical solution for real world deployment.
| no_new_dataset | 0.948394 |
2503.19240 | Hao Guo | Hao Guo, Jianfei Zhu, Wei Fan, Chunzhi Yi, Feng Jiang | Beyond Object Categories: Multi-Attribute Reference Understanding for
Visual Grounding | null | null | null | null | cs.CV cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Referring expression comprehension (REC) aims at achieving object
localization based on natural language descriptions. However, existing REC
approaches are constrained by object category descriptions and single-attribute
intention descriptions, hindering their application in real-world scenarios. In
natural human-robot interactions, users often express their desires through
individual states and intentions, accompanied by guiding gestures, rather than
detailed object descriptions. To address this challenge, we propose Multi-ref
EC, a novel task framework that integrates state descriptions, derived
intentions, and embodied gestures to locate target objects. We introduce the
State-Intention-Gesture Attributes Reference (SIGAR) dataset, which combines
state and intention expressions with embodied references. Through extensive
experiments with various baseline models on SIGAR, we demonstrate that properly
ordered multi-attribute references contribute to improved localization
performance, revealing that single-attribute reference is insufficient for
natural human-robot interaction scenarios. Our findings underscore the
importance of multi-attribute reference expressions in advancing
visual-language understanding.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 00:59:58 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Guo",
"Hao",
""
],
[
"Zhu",
"Jianfei",
""
],
[
"Fan",
"Wei",
""
],
[
"Yi",
"Chunzhi",
""
],
[
"Jiang",
"Feng",
""
]
] | TITLE: Beyond Object Categories: Multi-Attribute Reference Understanding for
Visual Grounding
ABSTRACT: Referring expression comprehension (REC) aims at achieving object
localization based on natural language descriptions. However, existing REC
approaches are constrained by object category descriptions and single-attribute
intention descriptions, hindering their application in real-world scenarios. In
natural human-robot interactions, users often express their desires through
individual states and intentions, accompanied by guiding gestures, rather than
detailed object descriptions. To address this challenge, we propose Multi-ref
EC, a novel task framework that integrates state descriptions, derived
intentions, and embodied gestures to locate target objects. We introduce the
State-Intention-Gesture Attributes Reference (SIGAR) dataset, which combines
state and intention expressions with embodied references. Through extensive
experiments with various baseline models on SIGAR, we demonstrate that properly
ordered multi-attribute references contribute to improved localization
performance, revealing that single-attribute reference is insufficient for
natural human-robot interaction scenarios. Our findings underscore the
importance of multi-attribute reference expressions in advancing
visual-language understanding.
| new_dataset | 0.955277 |
2503.19248 | Hanfei Yan | Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan | Limited-angle x-ray nano-tomography with machine-learning enabled
iterative reconstruction engine | null | null | null | null | cond-mat.mtrl-sci cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A long-standing challenge in tomography is the 'missing wedge' problem, which
arises when the acquisition of projection images within a certain angular range
is restricted due to geometrical constraints. This incomplete dataset results
in significant artifacts and poor resolution in the reconstructed image. To
tackle this challenge, we propose an approach dubbed Perception Fused Iterative
Tomography Reconstruction Engine, which integrates a convolutional neural
network (CNN) with perceptional knowledge as a smart regularizer into an
iterative solving engine. We employ the Alternating Direction Method of
Multipliers to optimize the solution in both physics and image domains, thereby
achieving a physically coherent and visually enhanced result. We demonstrate
the effectiveness of the proposed approach using various experimental datasets
obtained with different x-ray microscopy techniques. All show significantly
improved reconstruction even with a missing wedge of over 100 degrees - a
scenario where conventional methods fail. Notably, it also improves the
reconstruction in case of sparse projections, despite the network not being
specifically trained for that. This demonstrates the robustness and generality
of our method of addressing commonly occurring challenges in 3D x-ray imaging
applications for real-world problems.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 01:14:16 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhao",
"Chonghang",
""
],
[
"Ge",
"Mingyuan",
""
],
[
"Yang",
"Xiaogang",
""
],
[
"Chu",
"Yong S.",
""
],
[
"Yan",
"Hanfei",
""
]
] | TITLE: Limited-angle x-ray nano-tomography with machine-learning enabled
iterative reconstruction engine
ABSTRACT: A long-standing challenge in tomography is the 'missing wedge' problem, which
arises when the acquisition of projection images within a certain angular range
is restricted due to geometrical constraints. This incomplete dataset results
in significant artifacts and poor resolution in the reconstructed image. To
tackle this challenge, we propose an approach dubbed Perception Fused Iterative
Tomography Reconstruction Engine, which integrates a convolutional neural
network (CNN) with perceptional knowledge as a smart regularizer into an
iterative solving engine. We employ the Alternating Direction Method of
Multipliers to optimize the solution in both physics and image domains, thereby
achieving a physically coherent and visually enhanced result. We demonstrate
the effectiveness of the proposed approach using various experimental datasets
obtained with different x-ray microscopy techniques. All show significantly
improved reconstruction even with a missing wedge of over 100 degrees - a
scenario where conventional methods fail. Notably, it also improves the
reconstruction in case of sparse projections, despite the network not being
specifically trained for that. This demonstrates the robustness and generality
of our method of addressing commonly occurring challenges in 3D x-ray imaging
applications for real-world problems.
| no_new_dataset | 0.949012 |
2503.19253 | Zeqiang Wei | Zeqiang Wei, Kai Jin, Zeyi Hou, Kuan Song, Xiuzhuang Zhou | $L^2$FMamba: Lightweight Light Field Image Super-Resolution with State
Space Model | This work has been submitted to the IEEE for possible publication | null | null | null | eess.IV cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transformers bring significantly improved performance to the light field
image super-resolution task due to their long-range dependency modeling
capability. However, the inherently high computational complexity of their core
self-attention mechanism has increasingly hindered their advancement in this
task. To address this issue, we first introduce the LF-VSSM block, a novel
module inspired by progressive feature extraction, to efficiently capture
critical long-range spatial-angular dependencies in light field images. LF-VSSM
successively extracts spatial features within sub-aperture images,
spatial-angular features between sub-aperture images, and spatial-angular
features between light field image pixels. On this basis, we propose a
lightweight network, $L^2$FMamba (Lightweight Light Field Mamba), which
integrates the LF-VSSM block to leverage light field features for
super-resolution tasks while overcoming the computational challenges of
Transformer-based approaches. Extensive experiments on multiple light field
datasets demonstrate that our method reduces the number of parameters and
complexity while achieving superior super-resolution performance with faster
inference speed.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 01:24:52 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Wei",
"Zeqiang",
""
],
[
"Jin",
"Kai",
""
],
[
"Hou",
"Zeyi",
""
],
[
"Song",
"Kuan",
""
],
[
"Zhou",
"Xiuzhuang",
""
]
] | TITLE: $L^2$FMamba: Lightweight Light Field Image Super-Resolution with State
Space Model
ABSTRACT: Transformers bring significantly improved performance to the light field
image super-resolution task due to their long-range dependency modeling
capability. However, the inherently high computational complexity of their core
self-attention mechanism has increasingly hindered their advancement in this
task. To address this issue, we first introduce the LF-VSSM block, a novel
module inspired by progressive feature extraction, to efficiently capture
critical long-range spatial-angular dependencies in light field images. LF-VSSM
successively extracts spatial features within sub-aperture images,
spatial-angular features between sub-aperture images, and spatial-angular
features between light field image pixels. On this basis, we propose a
lightweight network, $L^2$FMamba (Lightweight Light Field Mamba), which
integrates the LF-VSSM block to leverage light field features for
super-resolution tasks while overcoming the computational challenges of
Transformer-based approaches. Extensive experiments on multiple light field
datasets demonstrate that our method reduces the number of parameters and
complexity while achieving superior super-resolution performance with faster
inference speed.
| no_new_dataset | 0.951097 |
2503.19263 | Fucai Ke | Fucai Ke, Vijay Kumar B G, Xingjian Leng, Zhixi Cai, Zaid Khan,
Weiqing Wang, Pari Delir Haghighi, Hamid Rezatofighi, Manmohan Chandraker | DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow
Generation & Instruct-Masking Tuning | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Visual reasoning (VR), which is crucial in many fields for enabling
human-like visual understanding, remains highly challenging. Recently,
compositional visual reasoning approaches, which leverage the reasoning
abilities of large language models (LLMs) with integrated tools to solve
problems, have shown promise as more effective strategies than end-to-end VR
methods. However, these approaches face limitations, as frozen LLMs lack tool
awareness in VR, leading to performance bottlenecks. While leveraging LLMs for
reasoning is widely used in other domains, they are not directly applicable to
VR due to limited training data, imperfect tools that introduce errors and
reduce data collection efficiency in VR, and challenging in fine-tuning on
noisy workflows. To address these challenges, we propose DWIM: i)
Discrepancy-aware training Workflow generation, which assesses tool usage and
extracts more viable workflows for training; and ii) Instruct-Masking
fine-tuning, which guides the model to only clone effective actions, enabling
the generation of more practical solutions. Our experiments demonstrate that
DWIM achieves state-of-the-art performance across various VR tasks, exhibiting
strong generalization on multiple widely-used datasets.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 01:57:59 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Ke",
"Fucai",
""
],
[
"G",
"Vijay Kumar B",
""
],
[
"Leng",
"Xingjian",
""
],
[
"Cai",
"Zhixi",
""
],
[
"Khan",
"Zaid",
""
],
[
"Wang",
"Weiqing",
""
],
[
"Haghighi",
"Pari Delir",
""
],
[
"Rezatofighi",
"Hamid",
""
],
[
"Chandraker",
"Manmohan",
""
]
] | TITLE: DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow
Generation & Instruct-Masking Tuning
ABSTRACT: Visual reasoning (VR), which is crucial in many fields for enabling
human-like visual understanding, remains highly challenging. Recently,
compositional visual reasoning approaches, which leverage the reasoning
abilities of large language models (LLMs) with integrated tools to solve
problems, have shown promise as more effective strategies than end-to-end VR
methods. However, these approaches face limitations, as frozen LLMs lack tool
awareness in VR, leading to performance bottlenecks. While leveraging LLMs for
reasoning is widely used in other domains, they are not directly applicable to
VR due to limited training data, imperfect tools that introduce errors and
reduce data collection efficiency in VR, and challenging in fine-tuning on
noisy workflows. To address these challenges, we propose DWIM: i)
Discrepancy-aware training Workflow generation, which assesses tool usage and
extracts more viable workflows for training; and ii) Instruct-Masking
fine-tuning, which guides the model to only clone effective actions, enabling
the generation of more practical solutions. Our experiments demonstrate that
DWIM achieves state-of-the-art performance across various VR tasks, exhibiting
strong generalization on multiple widely-used datasets.
| no_new_dataset | 0.949856 |
2503.19267 | Yang Yu | Songyi Gao, Zuolin Tu, Rong-Jun Qin, Yi-Hao Sun, Xiong-Hui Chen, Yang
Yu | NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning
with Extended Realistic Scenarios | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Offline reinforcement learning (RL) aims to learn from historical data
without requiring (costly) access to the environment. To facilitate offline RL
research, we previously introduced NeoRL, which highlighted that datasets from
real-world tasks are often conservative and limited. With years of experience
applying offline RL to various domains, we have identified additional
real-world challenges. These include extremely conservative data distributions
produced by deployed control systems, delayed action effects caused by
high-latency transitions, external factors arising from the uncontrollable
variance of transitions, and global safety constraints that are difficult to
evaluate during the decision-making process. These challenges are
underrepresented in previous benchmarks but frequently occur in real-world
tasks. To address this, we constructed the extended Near Real-World Offline RL
Benchmark (NeoRL-2), which consists of 7 datasets from 7 simulated tasks along
with their corresponding evaluation simulators. Benchmarking results from
state-of-the-art offline RL approaches demonstrate that current methods often
struggle to outperform the data-collection behavior policy, highlighting the
need for more effective methods. We hope NeoRL-2 will accelerate the
development of reinforcement learning algorithms for real-world applications.
The benchmark project page is available at https://github.com/polixir/NeoRL2.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 02:01:54 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Gao",
"Songyi",
""
],
[
"Tu",
"Zuolin",
""
],
[
"Qin",
"Rong-Jun",
""
],
[
"Sun",
"Yi-Hao",
""
],
[
"Chen",
"Xiong-Hui",
""
],
[
"Yu",
"Yang",
""
]
] | TITLE: NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning
with Extended Realistic Scenarios
ABSTRACT: Offline reinforcement learning (RL) aims to learn from historical data
without requiring (costly) access to the environment. To facilitate offline RL
research, we previously introduced NeoRL, which highlighted that datasets from
real-world tasks are often conservative and limited. With years of experience
applying offline RL to various domains, we have identified additional
real-world challenges. These include extremely conservative data distributions
produced by deployed control systems, delayed action effects caused by
high-latency transitions, external factors arising from the uncontrollable
variance of transitions, and global safety constraints that are difficult to
evaluate during the decision-making process. These challenges are
underrepresented in previous benchmarks but frequently occur in real-world
tasks. To address this, we constructed the extended Near Real-World Offline RL
Benchmark (NeoRL-2), which consists of 7 datasets from 7 simulated tasks along
with their corresponding evaluation simulators. Benchmarking results from
state-of-the-art offline RL approaches demonstrate that current methods often
struggle to outperform the data-collection behavior policy, highlighting the
need for more effective methods. We hope NeoRL-2 will accelerate the
development of reinforcement learning algorithms for real-world applications.
The benchmark project page is available at https://github.com/polixir/NeoRL2.
| no_new_dataset | 0.8474 |
2503.19268 | Ephraim Linder | Ephraim Linder, Sofya Raskhodnikova, Adam Smith, Thomas Steinke | Privately Evaluating Untrusted Black-Box Functions | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide tools for sharing sensitive data when the data curator doesn't
know in advance what questions an (untrusted) analyst might ask about the data.
The analyst can specify a program that they want the curator to run on the
dataset. We model the program as a black-box function $f$. We study
differentially private algorithms, called privacy wrappers, that, given
black-box access to a real-valued function $f$ and a sensitive dataset $x$,
output an accurate approximation to $f(x)$. The dataset $x$ is modeled as a
finite subset of a possibly infinite set $U$, in which each entry represents
data of one individual. A privacy wrapper calls $f$ on the dataset $x$ and on
some subsets of $x$ and returns either an approximation to $f(x)$ or a
nonresponse symbol $\perp$. The wrapper may also use additional information
(that is, parameters) provided by the analyst, but differential privacy is
required for all values of these parameters. Correct setting of these
parameters will ensure better accuracy of the wrapper. The bottleneck in the
running time of our wrappers is the number of calls to $f$, which we refer to
as queries. Our goal is to design wrappers with high accuracy and low query
complexity. We introduce a novel setting, the automated sensitivity detection
setting, where the analyst supplies the black-box function $f$ and the intended
(finite) range of $f$. In the previously considered setting, the claimed
sensitivity bound setting, the analyst supplies additional parameters that
describe the sensitivity of $f$. We design privacy wrappers for both settings
and show that our wrappers are nearly optimal in terms of accuracy, locality
(i.e., the depth of the local neighborhood of the dataset $x$ they explore),
and query complexity. In the claimed sensitivity bound setting, we provide the
first accuracy guarantees that have no dependence on the size of the universe
$U$.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 02:04:13 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Linder",
"Ephraim",
""
],
[
"Raskhodnikova",
"Sofya",
""
],
[
"Smith",
"Adam",
""
],
[
"Steinke",
"Thomas",
""
]
] | TITLE: Privately Evaluating Untrusted Black-Box Functions
ABSTRACT: We provide tools for sharing sensitive data when the data curator doesn't
know in advance what questions an (untrusted) analyst might ask about the data.
The analyst can specify a program that they want the curator to run on the
dataset. We model the program as a black-box function $f$. We study
differentially private algorithms, called privacy wrappers, that, given
black-box access to a real-valued function $f$ and a sensitive dataset $x$,
output an accurate approximation to $f(x)$. The dataset $x$ is modeled as a
finite subset of a possibly infinite set $U$, in which each entry represents
data of one individual. A privacy wrapper calls $f$ on the dataset $x$ and on
some subsets of $x$ and returns either an approximation to $f(x)$ or a
nonresponse symbol $\perp$. The wrapper may also use additional information
(that is, parameters) provided by the analyst, but differential privacy is
required for all values of these parameters. Correct setting of these
parameters will ensure better accuracy of the wrapper. The bottleneck in the
running time of our wrappers is the number of calls to $f$, which we refer to
as queries. Our goal is to design wrappers with high accuracy and low query
complexity. We introduce a novel setting, the automated sensitivity detection
setting, where the analyst supplies the black-box function $f$ and the intended
(finite) range of $f$. In the previously considered setting, the claimed
sensitivity bound setting, the analyst supplies additional parameters that
describe the sensitivity of $f$. We design privacy wrappers for both settings
and show that our wrappers are nearly optimal in terms of accuracy, locality
(i.e., the depth of the local neighborhood of the dataset $x$ they explore),
and query complexity. In the claimed sensitivity bound setting, we provide the
first accuracy guarantees that have no dependence on the size of the universe
$U$.
| no_new_dataset | 0.941708 |
2503.19276 | Ben Rahman Dr. | Ben Rahman | Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding
with Large Language Models for Advanced Vision Applications | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic segmentation has made significant strides in pixel-level image
understanding, yet it remains limited in capturing contextual and semantic
relationships between objects. Current models, such as CNN and
Transformer-based architectures, excel at identifying pixel-level features but
fail to distinguish semantically similar objects (e.g., "doctor" vs. "nurse" in
a hospital scene) or understand complex contextual scenarios (e.g.,
differentiating a running child from a regular pedestrian in autonomous
driving). To address these limitations, we proposed a novel Context-Aware
Semantic Segmentation framework that integrates Large Language Models (LLMs)
with state-of-the-art vision backbones. Our hybrid model leverages the Swin
Transformer for robust visual feature extraction and GPT-4 for enriching
semantic understanding through text embeddings. A Cross-Attention Mechanism is
introduced to align vision and language features, enabling the model to reason
about context more effectively. Additionally, Graph Neural Networks (GNNs) are
employed to model object relationships within the scene, capturing dependencies
that are overlooked by traditional models. Experimental results on benchmark
datasets (e.g., COCO, Cityscapes) demonstrate that our approach outperforms the
existing methods in both pixel-level accuracy (mIoU) and contextual
understanding (mAP). This work bridges the gap between vision and language,
paving the path for more intelligent and context-aware vision systems in
applications including autonomous driving, medical imaging, and robotics.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 02:12:35 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Rahman",
"Ben",
""
]
] | TITLE: Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding
with Large Language Models for Advanced Vision Applications
ABSTRACT: Semantic segmentation has made significant strides in pixel-level image
understanding, yet it remains limited in capturing contextual and semantic
relationships between objects. Current models, such as CNN and
Transformer-based architectures, excel at identifying pixel-level features but
fail to distinguish semantically similar objects (e.g., "doctor" vs. "nurse" in
a hospital scene) or understand complex contextual scenarios (e.g.,
differentiating a running child from a regular pedestrian in autonomous
driving). To address these limitations, we proposed a novel Context-Aware
Semantic Segmentation framework that integrates Large Language Models (LLMs)
with state-of-the-art vision backbones. Our hybrid model leverages the Swin
Transformer for robust visual feature extraction and GPT-4 for enriching
semantic understanding through text embeddings. A Cross-Attention Mechanism is
introduced to align vision and language features, enabling the model to reason
about context more effectively. Additionally, Graph Neural Networks (GNNs) are
employed to model object relationships within the scene, capturing dependencies
that are overlooked by traditional models. Experimental results on benchmark
datasets (e.g., COCO, Cityscapes) demonstrate that our approach outperforms the
existing methods in both pixel-level accuracy (mIoU) and contextual
understanding (mAP). This work bridges the gap between vision and language,
paving the path for more intelligent and context-aware vision systems in
applications including autonomous driving, medical imaging, and robotics.
| no_new_dataset | 0.947235 |
2503.19281 | Feiyang Wang | Feiyang Wang and Xiaomin Yu and Wangyu Wu | CubeRobot: Grounding Language in Rubik's Cube Manipulation via
Vision-Language Model | null | null | null | null | cs.RO cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Proving Rubik's Cube theorems at the high level represents a notable
milestone in human-level spatial imagination and logic thinking and reasoning.
Traditional Rubik's Cube robots, relying on complex vision systems and fixed
algorithms, often struggle to adapt to complex and dynamic scenarios. To
overcome this limitation, we introduce CubeRobot, a novel vision-language model
(VLM) tailored for solving 3x3 Rubik's Cubes, empowering embodied agents with
multimodal understanding and execution capabilities. We used the CubeCoT image
dataset, which contains multiple-level tasks (43 subtasks in total) that humans
are unable to handle, encompassing various cube states. We incorporate a
dual-loop VisionCoT architecture and Memory Stream, a paradigm for extracting
task-related features from VLM-generated planning queries, thus enabling
CubeRobot to independent planning, decision-making, reflection and separate
management of high- and low-level Rubik's Cube tasks. Furthermore, in low-level
Rubik's Cube restoration tasks, CubeRobot achieved a high accuracy rate of
100%, similar to 100% in medium-level tasks, and achieved an accuracy rate of
80% in high-level tasks.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 02:23:47 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Wang",
"Feiyang",
""
],
[
"Yu",
"Xiaomin",
""
],
[
"Wu",
"Wangyu",
""
]
] | TITLE: CubeRobot: Grounding Language in Rubik's Cube Manipulation via
Vision-Language Model
ABSTRACT: Proving Rubik's Cube theorems at the high level represents a notable
milestone in human-level spatial imagination and logic thinking and reasoning.
Traditional Rubik's Cube robots, relying on complex vision systems and fixed
algorithms, often struggle to adapt to complex and dynamic scenarios. To
overcome this limitation, we introduce CubeRobot, a novel vision-language model
(VLM) tailored for solving 3x3 Rubik's Cubes, empowering embodied agents with
multimodal understanding and execution capabilities. We used the CubeCoT image
dataset, which contains multiple-level tasks (43 subtasks in total) that humans
are unable to handle, encompassing various cube states. We incorporate a
dual-loop VisionCoT architecture and Memory Stream, a paradigm for extracting
task-related features from VLM-generated planning queries, thus enabling
CubeRobot to independent planning, decision-making, reflection and separate
management of high- and low-level Rubik's Cube tasks. Furthermore, in low-level
Rubik's Cube restoration tasks, CubeRobot achieved a high accuracy rate of
100%, similar to 100% in medium-level tasks, and achieved an accuracy rate of
80% in high-level tasks.
| no_new_dataset | 0.948298 |
2503.19296 | Haoqiang Lin | Haoqiang Lin and Haokun Wen and Xuemeng Song and Meng Liu and Yupeng
Hu and Liqiang Nie | Fine-grained Textual Inversion Network for Zero-Shot Composed Image
Retrieval | null | null | 10.1145/3626772.3657831 | null | cs.CV cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Composed Image Retrieval (CIR) allows users to search target images with a
multimodal query, comprising a reference image and a modification text that
describes the user's modification demand over the reference image.
Nevertheless, due to the expensive labor cost of training data annotation,
recent researchers have shifted to the challenging task of zero-shot CIR
(ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer
ZS-CIR studies focus on converting the CIR task into a standard text-to-image
retrieval task by pre-training a textual inversion network that can map a given
image into a single pseudo-word token. Despite their significant progress,
their coarse-grained textual inversion may be insufficient to capture the full
content of the image accurately. To overcome this issue, in this work, we
propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named
FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained
pseudo-word token mapping and tri-wise caption-based semantic regularization.
The former maps the image into a subject-oriented pseudo-word token and several
attribute-oriented pseudo-word tokens to comprehensively express the image in
the textual form, while the latter works on jointly aligning the fine-grained
pseudo-word tokens to the real-word token embedding space based on a
BLIP-generated image caption template. Extensive experiments conducted on three
benchmark datasets demonstrate the superiority of our proposed method.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 02:51:25 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Lin",
"Haoqiang",
""
],
[
"Wen",
"Haokun",
""
],
[
"Song",
"Xuemeng",
""
],
[
"Liu",
"Meng",
""
],
[
"Hu",
"Yupeng",
""
],
[
"Nie",
"Liqiang",
""
]
] | TITLE: Fine-grained Textual Inversion Network for Zero-Shot Composed Image
Retrieval
ABSTRACT: Composed Image Retrieval (CIR) allows users to search target images with a
multimodal query, comprising a reference image and a modification text that
describes the user's modification demand over the reference image.
Nevertheless, due to the expensive labor cost of training data annotation,
recent researchers have shifted to the challenging task of zero-shot CIR
(ZS-CIR), which targets fulfilling CIR without annotated triplets. The pioneer
ZS-CIR studies focus on converting the CIR task into a standard text-to-image
retrieval task by pre-training a textual inversion network that can map a given
image into a single pseudo-word token. Despite their significant progress,
their coarse-grained textual inversion may be insufficient to capture the full
content of the image accurately. To overcome this issue, in this work, we
propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named
FTI4CIR. In particular, FTI4CIR comprises two main components: fine-grained
pseudo-word token mapping and tri-wise caption-based semantic regularization.
The former maps the image into a subject-oriented pseudo-word token and several
attribute-oriented pseudo-word tokens to comprehensively express the image in
the textual form, while the latter works on jointly aligning the fine-grained
pseudo-word tokens to the real-word token embedding space based on a
BLIP-generated image caption template. Extensive experiments conducted on three
benchmark datasets demonstrate the superiority of our proposed method.
| no_new_dataset | 0.948965 |
2503.19303 | Hanshuo Qiu | Hanshuo Qiu, Jie Jiang, Ruoli Yang, Lixin Zhan, Jizhao Liu | BIMII-Net: Brain-Inspired Multi-Iterative Interactive Network for RGB-T
Road Scene Semantic Segmentation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RGB-T road scene semantic segmentation enhances visual scene understanding in
complex environments characterized by inadequate illumination or occlusion by
fusing information from RGB and thermal images. Nevertheless, existing RGB-T
semantic segmentation models typically depend on simple addition or
concatenation strategies or ignore the differences between information at
different levels. To address these issues, we proposed a novel RGB-T road scene
semantic segmentation network called Brain-Inspired Multi-Iteration Interaction
Network (BIMII-Net). First, to meet the requirements of accurate texture and
local information extraction in road scenarios like autonomous driving, we
proposed a deep continuous-coupled neural network (DCCNN) architecture based on
a brain-inspired model. Second, to enhance the interaction and expression
capabilities among multi-modal information, we designed a cross explicit
attention-enhanced fusion module (CEAEF-Module) in the feature fusion stage of
BIMII-Net to effectively integrate features at different levels. Finally, we
constructed a complementary interactive multi-layer decoder structure,
incorporating the shallow-level feature iteration module (SFI-Module), the
deep-level feature iteration module (DFI-Module), and the multi-feature
enhancement module (MFE-Module) to collaboratively extract texture details and
global skeleton information, with multi-module joint supervision further
optimizing the segmentation results. Experimental results demonstrate that
BIMII-Net achieves state-of-the-art (SOTA) performance in the brain-inspired
computing domain and outperforms most existing RGB-T semantic segmentation
methods. It also exhibits strong generalization capabilities on multiple RGB-T
datasets, proving the effectiveness of brain-inspired computer models in
multi-modal image segmentation tasks.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:09:46 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Qiu",
"Hanshuo",
""
],
[
"Jiang",
"Jie",
""
],
[
"Yang",
"Ruoli",
""
],
[
"Zhan",
"Lixin",
""
],
[
"Liu",
"Jizhao",
""
]
] | TITLE: BIMII-Net: Brain-Inspired Multi-Iterative Interactive Network for RGB-T
Road Scene Semantic Segmentation
ABSTRACT: RGB-T road scene semantic segmentation enhances visual scene understanding in
complex environments characterized by inadequate illumination or occlusion by
fusing information from RGB and thermal images. Nevertheless, existing RGB-T
semantic segmentation models typically depend on simple addition or
concatenation strategies or ignore the differences between information at
different levels. To address these issues, we proposed a novel RGB-T road scene
semantic segmentation network called Brain-Inspired Multi-Iteration Interaction
Network (BIMII-Net). First, to meet the requirements of accurate texture and
local information extraction in road scenarios like autonomous driving, we
proposed a deep continuous-coupled neural network (DCCNN) architecture based on
a brain-inspired model. Second, to enhance the interaction and expression
capabilities among multi-modal information, we designed a cross explicit
attention-enhanced fusion module (CEAEF-Module) in the feature fusion stage of
BIMII-Net to effectively integrate features at different levels. Finally, we
constructed a complementary interactive multi-layer decoder structure,
incorporating the shallow-level feature iteration module (SFI-Module), the
deep-level feature iteration module (DFI-Module), and the multi-feature
enhancement module (MFE-Module) to collaboratively extract texture details and
global skeleton information, with multi-module joint supervision further
optimizing the segmentation results. Experimental results demonstrate that
BIMII-Net achieves state-of-the-art (SOTA) performance in the brain-inspired
computing domain and outperforms most existing RGB-T semantic segmentation
methods. It also exhibits strong generalization capabilities on multiple RGB-T
datasets, proving the effectiveness of brain-inspired computer models in
multi-modal image segmentation tasks.
| no_new_dataset | 0.949623 |
2503.19306 | Amjad Ali | Amjad Ali and Zardad Khan and Saeed Aldahmani | Centroid Decision Forest | This article has 11 pages, 6 figures, and 3 tables and has been
submitted to the "IEEE Transactions on Pattern Analysis and Machine
Intelligence" journal | null | null | null | stat.ML cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper introduces the centroid decision forest (CDF), a novel ensemble
learning framework that redefines the splitting strategy and tree building in
the ordinary decision trees for high-dimensional classification. The splitting
approach in CDF differs from the traditional decision trees in theat the class
separability score (CSS) determines the selection of the most discriminative
features at each node to construct centroids of the partitions (daughter
nodes). The splitting criterion uses the Euclidean distance measurements from
each class centroid to achieve a splitting mechanism that is more flexible and
robust. Centroids are constructed by computing the mean feature values of the
selected features for each class, ensuring a class-representative division of
the feature space. This centroid-driven approach enables CDF to capture complex
class structures while maintaining interpretability and scalability. To
evaluate CDF, 23 high-dimensional datasets are used to assess its performance
against different state-of-the-art classifiers through classification accuracy
and Cohen's kappa statistic. The experimental results show that CDF outperforms
the conventional methods establishing its effectiveness and flexibility for
high-dimensional classification problems.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:12:52 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Ali",
"Amjad",
""
],
[
"Khan",
"Zardad",
""
],
[
"Aldahmani",
"Saeed",
""
]
] | TITLE: Centroid Decision Forest
ABSTRACT: This paper introduces the centroid decision forest (CDF), a novel ensemble
learning framework that redefines the splitting strategy and tree building in
the ordinary decision trees for high-dimensional classification. The splitting
approach in CDF differs from the traditional decision trees in theat the class
separability score (CSS) determines the selection of the most discriminative
features at each node to construct centroids of the partitions (daughter
nodes). The splitting criterion uses the Euclidean distance measurements from
each class centroid to achieve a splitting mechanism that is more flexible and
robust. Centroids are constructed by computing the mean feature values of the
selected features for each class, ensuring a class-representative division of
the feature space. This centroid-driven approach enables CDF to capture complex
class structures while maintaining interpretability and scalability. To
evaluate CDF, 23 high-dimensional datasets are used to assess its performance
against different state-of-the-art classifiers through classification accuracy
and Cohen's kappa statistic. The experimental results show that CDF outperforms
the conventional methods establishing its effectiveness and flexibility for
high-dimensional classification problems.
| no_new_dataset | 0.945551 |
2503.19307 | Zhuoran Zhao | Zhuoran Zhao, Linlin Yang, Pengzhan Sun, Pan Hui, Angela Yao | Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation | Accepted to CVPR2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent synthetic 3D human datasets for the face, body, and hands have pushed
the limits on photorealism. Face recognition and body pose estimation have
achieved state-of-the-art performance using synthetic training data alone, but
for the hand, there is still a large synthetic-to-real gap. This paper presents
the first systematic study of the synthetic-to-real gap of 3D hand pose
estimation. We analyze the gap and identify key components such as the forearm,
image frequency statistics, hand pose, and object occlusions. To facilitate our
analysis, we propose a data synthesis pipeline to synthesize high-quality data.
We demonstrate that synthetic hand data can achieve the same level of accuracy
as real data when integrating our identified components, paving the path to use
synthetic data alone for hand pose estimation. Code and data are available at:
https://github.com/delaprada/HandSynthesis.git.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:13:23 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhao",
"Zhuoran",
""
],
[
"Yang",
"Linlin",
""
],
[
"Sun",
"Pengzhan",
""
],
[
"Hui",
"Pan",
""
],
[
"Yao",
"Angela",
""
]
] | TITLE: Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
ABSTRACT: Recent synthetic 3D human datasets for the face, body, and hands have pushed
the limits on photorealism. Face recognition and body pose estimation have
achieved state-of-the-art performance using synthetic training data alone, but
for the hand, there is still a large synthetic-to-real gap. This paper presents
the first systematic study of the synthetic-to-real gap of 3D hand pose
estimation. We analyze the gap and identify key components such as the forearm,
image frequency statistics, hand pose, and object occlusions. To facilitate our
analysis, we propose a data synthesis pipeline to synthesize high-quality data.
We demonstrate that synthetic hand data can achieve the same level of accuracy
as real data when integrating our identified components, paving the path to use
synthetic data alone for hand pose estimation. Code and data are available at:
https://github.com/delaprada/HandSynthesis.git.
| no_new_dataset | 0.944536 |
2503.19309 | Gollam Rabby | Gollam Rabby, Diyana Muhammed, Prasenjit Mitra, S\"oren Auer | Iterative Hypothesis Generation for Scientific Discovery with Monte
Carlo Nash Equilibrium Self-Refining Trees | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Scientific hypothesis generation is a fundamentally challenging task in
research, requiring the synthesis of novel and empirically grounded insights.
Traditional approaches rely on human intuition and domain expertise, while
purely large language model (LLM) based methods often struggle to produce
hypotheses that are both innovative and reliable. To address these limitations,
we propose the Monte Carlo Nash Equilibrium Self-Refine Tree (MC-NEST), a novel
framework that integrates Monte Carlo Tree Search with Nash Equilibrium
strategies to iteratively refine and validate hypotheses. MC-NEST dynamically
balances exploration and exploitation through adaptive sampling strategies,
which prioritize high-potential hypotheses while maintaining diversity in the
search space. We demonstrate the effectiveness of MC-NEST through comprehensive
experiments across multiple domains, including biomedicine, social science, and
computer science. MC-NEST achieves average scores of 2.65, 2.74, and 2.80 (on a
1-3 scale) for novelty, clarity, significance, and verifiability metrics on the
social science, computer science, and biomedicine datasets, respectively,
outperforming state-of-the-art prompt-based methods, which achieve 2.36, 2.51,
and 2.52 on the same datasets. These results underscore MC-NEST's ability to
generate high-quality, empirically grounded hypotheses across diverse domains.
Furthermore, MC-NEST facilitates structured human-AI collaboration, ensuring
that LLMs augment human creativity rather than replace it. By addressing key
challenges such as iterative refinement and the exploration-exploitation
balance, MC-NEST sets a new benchmark in automated hypothesis generation.
Additionally, MC-NEST's ethical design enables responsible AI use, emphasizing
transparency and human supervision in hypothesis generation.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:14:53 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Rabby",
"Gollam",
""
],
[
"Muhammed",
"Diyana",
""
],
[
"Mitra",
"Prasenjit",
""
],
[
"Auer",
"Sören",
""
]
] | TITLE: Iterative Hypothesis Generation for Scientific Discovery with Monte
Carlo Nash Equilibrium Self-Refining Trees
ABSTRACT: Scientific hypothesis generation is a fundamentally challenging task in
research, requiring the synthesis of novel and empirically grounded insights.
Traditional approaches rely on human intuition and domain expertise, while
purely large language model (LLM) based methods often struggle to produce
hypotheses that are both innovative and reliable. To address these limitations,
we propose the Monte Carlo Nash Equilibrium Self-Refine Tree (MC-NEST), a novel
framework that integrates Monte Carlo Tree Search with Nash Equilibrium
strategies to iteratively refine and validate hypotheses. MC-NEST dynamically
balances exploration and exploitation through adaptive sampling strategies,
which prioritize high-potential hypotheses while maintaining diversity in the
search space. We demonstrate the effectiveness of MC-NEST through comprehensive
experiments across multiple domains, including biomedicine, social science, and
computer science. MC-NEST achieves average scores of 2.65, 2.74, and 2.80 (on a
1-3 scale) for novelty, clarity, significance, and verifiability metrics on the
social science, computer science, and biomedicine datasets, respectively,
outperforming state-of-the-art prompt-based methods, which achieve 2.36, 2.51,
and 2.52 on the same datasets. These results underscore MC-NEST's ability to
generate high-quality, empirically grounded hypotheses across diverse domains.
Furthermore, MC-NEST facilitates structured human-AI collaboration, ensuring
that LLMs augment human creativity rather than replace it. By addressing key
challenges such as iterative refinement and the exploration-exploitation
balance, MC-NEST sets a new benchmark in automated hypothesis generation.
Additionally, MC-NEST's ethical design enables responsible AI use, emphasizing
transparency and human supervision in hypothesis generation.
| no_new_dataset | 0.946001 |
2503.19311 | Weizhi Chen | Weizhi Chen, Jingbo Chen, Yupeng Deng, Jiansheng Chen, Yuman Feng,
Zhihao Xi, Diyou Liu, Kai Li, Yu Meng | LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing
Image with Longer Text | 17 pages, 12 figures | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study addresses the technical bottlenecks in handling long text and the
"hallucination" issue caused by insufficient short text information in remote
sensing vision-language foundation models (VLFM). We propose a novel
vision-language foundation model, LRSCLIP, and a multimodal dataset, LRS2M. The
main contributions are as follows: (1) By integrating multi-source remote
sensing data and adopting a large language model labeling strategy, we
construct the LRS2M dataset, which contains 2 million image-text pairs,
providing both short and long texts for the first time, thus solving the
problem of semantic granularity limitations in existing datasets; (2) The
design of the LRSCLIP architecture based on Long-CLIP's KPS module, which
extends CLIP's text processing capacity and achieves fine-grained cross-modal
feature alignment through a dual-text loss weighting mechanism. Experimental
results show that LRSCLIP improves retrieval accuracy by 10\%-20\% over the
Long-CLIP baseline in the zero-shot long-text cross-modal retrieval task. For
the zero-shot short-text cross-modal retrieval task, LRSCLIP achieves
improvements over the current best model, GeoRSCLIP, with increases of 0.17\%,
0.67\%, and 0.92\% in Text to Image R@1, Image to Text R@1, and mR on RSITMD,
respectively, and 0.04\%, 2.93\%, and 1.28\% on RSICD. In the zero-shot image
classification task (average accuracy=75.75\%) and semantic localization task
(Rmi=0.7653), LRSCLIP achieves state-of-the-art performance. These results
validate the dual advantages of fine-grained semantic understanding and global
feature matching in LRSCLIP. This work provides a new benchmark model and data
support for remote sensing multimodal learning. The related code has been open
source and is available at https://github.com/MitsuiChen14/LRSCLIP.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:17:42 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Chen",
"Weizhi",
""
],
[
"Chen",
"Jingbo",
""
],
[
"Deng",
"Yupeng",
""
],
[
"Chen",
"Jiansheng",
""
],
[
"Feng",
"Yuman",
""
],
[
"Xi",
"Zhihao",
""
],
[
"Liu",
"Diyou",
""
],
[
"Li",
"Kai",
""
],
[
"Meng",
"Yu",
""
]
] | TITLE: LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing
Image with Longer Text
ABSTRACT: This study addresses the technical bottlenecks in handling long text and the
"hallucination" issue caused by insufficient short text information in remote
sensing vision-language foundation models (VLFM). We propose a novel
vision-language foundation model, LRSCLIP, and a multimodal dataset, LRS2M. The
main contributions are as follows: (1) By integrating multi-source remote
sensing data and adopting a large language model labeling strategy, we
construct the LRS2M dataset, which contains 2 million image-text pairs,
providing both short and long texts for the first time, thus solving the
problem of semantic granularity limitations in existing datasets; (2) The
design of the LRSCLIP architecture based on Long-CLIP's KPS module, which
extends CLIP's text processing capacity and achieves fine-grained cross-modal
feature alignment through a dual-text loss weighting mechanism. Experimental
results show that LRSCLIP improves retrieval accuracy by 10\%-20\% over the
Long-CLIP baseline in the zero-shot long-text cross-modal retrieval task. For
the zero-shot short-text cross-modal retrieval task, LRSCLIP achieves
improvements over the current best model, GeoRSCLIP, with increases of 0.17\%,
0.67\%, and 0.92\% in Text to Image R@1, Image to Text R@1, and mR on RSITMD,
respectively, and 0.04\%, 2.93\%, and 1.28\% on RSICD. In the zero-shot image
classification task (average accuracy=75.75\%) and semantic localization task
(Rmi=0.7653), LRSCLIP achieves state-of-the-art performance. These results
validate the dual advantages of fine-grained semantic understanding and global
feature matching in LRSCLIP. This work provides a new benchmark model and data
support for remote sensing multimodal learning. The related code has been open
source and is available at https://github.com/MitsuiChen14/LRSCLIP.
| no_new_dataset | 0.948394 |
2503.19312 | JIqi Liao | Jiaqi Liao, Zhengyuan Yang, Linjie Li, Dianqi Li, Kevin Lin, Yu Cheng,
Lijuan Wang | ImageGen-CoT: Enhancing Text-to-Image In-context Learning with
Chain-of-Thought Reasoning | Project Page: https://ImageGen-CoT.github.io/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we study the problem of Text-to-Image In-Context Learning
(T2I-ICL). While Unified Multimodal LLMs (MLLMs) have advanced rapidly in
recent years, they struggle with contextual reasoning in T2I-ICL scenarios. To
address this limitation, we propose a novel framework that incorporates a
thought process called ImageGen-CoT prior to image generation. To avoid
generating unstructured ineffective reasoning steps, we develop an automatic
pipeline to curate a high-quality ImageGen-CoT dataset. We then fine-tune MLLMs
using this dataset to enhance their contextual reasoning capabilities. To
further enhance performance, we explore test-time scale-up strategies and
propose a novel hybrid scaling approach. This approach first generates multiple
ImageGen-CoT chains and then produces multiple images for each chain via
sampling. Extensive experiments demonstrate the effectiveness of our proposed
method. Notably, fine-tuning with the ImageGen-CoT dataset leads to a
substantial 80\% performance gain for SEED-X on T2I-ICL tasks. See our project
page at https://ImageGen-CoT.github.io/. Code and model weights will be
open-sourced.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:18:46 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Liao",
"Jiaqi",
""
],
[
"Yang",
"Zhengyuan",
""
],
[
"Li",
"Linjie",
""
],
[
"Li",
"Dianqi",
""
],
[
"Lin",
"Kevin",
""
],
[
"Cheng",
"Yu",
""
],
[
"Wang",
"Lijuan",
""
]
] | TITLE: ImageGen-CoT: Enhancing Text-to-Image In-context Learning with
Chain-of-Thought Reasoning
ABSTRACT: In this work, we study the problem of Text-to-Image In-Context Learning
(T2I-ICL). While Unified Multimodal LLMs (MLLMs) have advanced rapidly in
recent years, they struggle with contextual reasoning in T2I-ICL scenarios. To
address this limitation, we propose a novel framework that incorporates a
thought process called ImageGen-CoT prior to image generation. To avoid
generating unstructured ineffective reasoning steps, we develop an automatic
pipeline to curate a high-quality ImageGen-CoT dataset. We then fine-tune MLLMs
using this dataset to enhance their contextual reasoning capabilities. To
further enhance performance, we explore test-time scale-up strategies and
propose a novel hybrid scaling approach. This approach first generates multiple
ImageGen-CoT chains and then produces multiple images for each chain via
sampling. Extensive experiments demonstrate the effectiveness of our proposed
method. Notably, fine-tuning with the ImageGen-CoT dataset leads to a
substantial 80\% performance gain for SEED-X on T2I-ICL tasks. See our project
page at https://ImageGen-CoT.github.io/. Code and model weights will be
open-sourced.
| new_dataset | 0.51978 |
2503.19324 | Qi Li | Qi Li | How to optimize K-means? | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Center-based clustering algorithms (e.g., K-means) are popular for clustering
tasks, but they usually struggle to achieve high accuracy on complex datasets.
We believe the main reason is that traditional center-based clustering
algorithms identify only one clustering center in each cluster. Once the
distribution of the dataset is complex, a single clustering center cannot
strongly represent distant objects within the cluster. How to optimize the
existing center-based clustering algorithms will be valuable research. In this
paper, we propose a general optimization method called ECAC, and it can
optimize different center-based clustering algorithms. ECAC is independent of
the clustering principle and is embedded as a component between the center
process and the category assignment process of center-based clustering
algorithms. Specifically, ECAC identifies several extended-centers for each
clustering center. The extended-centers will act as relays to expand the
representative capability of the clustering center in the complex cluster, thus
improving the accuracy of center-based clustering algorithms. We conducted
numerous experiments to verify the robustness and effectiveness of ECAC. ECAC
is robust to diverse datasets and diverse clustering centers. After ECAC
optimization, the accuracy (NMI as well as RI) of center-based clustering
algorithms improves by an average of 33.4% and 64.1%, respectively, and even
K-means accurately identifies complex-shaped clusters.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:37:52 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Li",
"Qi",
""
]
] | TITLE: How to optimize K-means?
ABSTRACT: Center-based clustering algorithms (e.g., K-means) are popular for clustering
tasks, but they usually struggle to achieve high accuracy on complex datasets.
We believe the main reason is that traditional center-based clustering
algorithms identify only one clustering center in each cluster. Once the
distribution of the dataset is complex, a single clustering center cannot
strongly represent distant objects within the cluster. How to optimize the
existing center-based clustering algorithms will be valuable research. In this
paper, we propose a general optimization method called ECAC, and it can
optimize different center-based clustering algorithms. ECAC is independent of
the clustering principle and is embedded as a component between the center
process and the category assignment process of center-based clustering
algorithms. Specifically, ECAC identifies several extended-centers for each
clustering center. The extended-centers will act as relays to expand the
representative capability of the clustering center in the complex cluster, thus
improving the accuracy of center-based clustering algorithms. We conducted
numerous experiments to verify the robustness and effectiveness of ECAC. ECAC
is robust to diverse datasets and diverse clustering centers. After ECAC
optimization, the accuracy (NMI as well as RI) of center-based clustering
algorithms improves by an average of 33.4% and 64.1%, respectively, and even
K-means accurately identifies complex-shaped clusters.
| no_new_dataset | 0.951459 |
2503.19329 | Yongting Hu | Yongting Hu, Yuxin Lin, Chengliang Liu, Xiaoling Luo, Xiaoyan Dou,
Qihao Xu, Yong Xu | Wavelet-based Global-Local Interaction Network with Cross-Attention for
Multi-View Diabetic Retinopathy Detection | Accepted by IEEE International Conference on Multimedia & Expo (ICME)
2025 | null | null | null | eess.IV cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-view diabetic retinopathy (DR) detection has recently emerged as a
promising method to address the issue of incomplete lesions faced by
single-view DR. However, it is still challenging due to the variable sizes and
scattered locations of lesions. Furthermore, existing multi-view DR methods
typically merge multiple views without considering the correlations and
redundancies of lesion information across them. Therefore, we propose a novel
method to overcome the challenges of difficult lesion information learning and
inadequate multi-view fusion. Specifically, we introduce a two-branch network
to obtain both local lesion features and their global dependencies. The
high-frequency component of the wavelet transform is used to exploit lesion
edge information, which is then enhanced by global semantic to facilitate
difficult lesion learning. Additionally, we present a cross-view fusion module
to improve multi-view fusion and reduce redundancy. Experimental results on
large public datasets demonstrate the effectiveness of our method. The code is
open sourced on https://github.com/HuYongting/WGLIN.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:44:57 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Hu",
"Yongting",
""
],
[
"Lin",
"Yuxin",
""
],
[
"Liu",
"Chengliang",
""
],
[
"Luo",
"Xiaoling",
""
],
[
"Dou",
"Xiaoyan",
""
],
[
"Xu",
"Qihao",
""
],
[
"Xu",
"Yong",
""
]
] | TITLE: Wavelet-based Global-Local Interaction Network with Cross-Attention for
Multi-View Diabetic Retinopathy Detection
ABSTRACT: Multi-view diabetic retinopathy (DR) detection has recently emerged as a
promising method to address the issue of incomplete lesions faced by
single-view DR. However, it is still challenging due to the variable sizes and
scattered locations of lesions. Furthermore, existing multi-view DR methods
typically merge multiple views without considering the correlations and
redundancies of lesion information across them. Therefore, we propose a novel
method to overcome the challenges of difficult lesion information learning and
inadequate multi-view fusion. Specifically, we introduce a two-branch network
to obtain both local lesion features and their global dependencies. The
high-frequency component of the wavelet transform is used to exploit lesion
edge information, which is then enhanced by global semantic to facilitate
difficult lesion learning. Additionally, we present a cross-view fusion module
to improve multi-view fusion and reduce redundancy. Experimental results on
large public datasets demonstrate the effectiveness of our method. The code is
open sourced on https://github.com/HuYongting/WGLIN.
| no_new_dataset | 0.948251 |
2503.19331 | Chau Pham | Chau Pham, Juan C. Caicedo, Bryan A. Plummer | ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel
Vision Transformers for Improved Cross-Channel Learning | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prior work using Masked Autoencoders (MAEs) typically relies on random patch
masking based on the assumption that images have significant redundancies
across different channels, allowing for the reconstruction of masked content
using cross-channel correlations. However, this assumption does not hold in
Multi-Channel Imaging (MCI), where channels may provide complementary
information with minimal feature overlap. Thus, these MAEs primarily learn
local structures within individual channels from patch reconstruction, failing
to fully leverage cross-channel interactions and limiting their MCI
effectiveness. In this paper, we present ChA-MAEViT, an MAE-based method that
enhances feature learning across MCI channels via four key strategies: (1)
dynamic channel-patch masking, which compels the model to reconstruct missing
channels in addition to masked patches, thereby enhancing cross-channel
dependencies and improving robustness to varying channel configurations; (2)
memory tokens, which serve as long-term memory aids to promote information
sharing across channels, addressing the challenges of reconstructing
structurally diverse channels; (3) hybrid token fusion module, which merges
fine-grained patch tokens with a global class token to capture richer
representations; and (4) Channel-Aware Decoder, a lightweight decoder utilizes
channel tokens to effectively reconstruct image patches. Experiments on
satellite and microscopy datasets, CHAMMI, JUMP-CP, and So2Sat, show that
ChA-MAEViT significantly outperforms state-of-the-art MCI-ViTs by 3.0-21.5%,
highlighting the importance of cross-channel interactions in MCI.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:45:59 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Pham",
"Chau",
""
],
[
"Caicedo",
"Juan C.",
""
],
[
"Plummer",
"Bryan A.",
""
]
] | TITLE: ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel
Vision Transformers for Improved Cross-Channel Learning
ABSTRACT: Prior work using Masked Autoencoders (MAEs) typically relies on random patch
masking based on the assumption that images have significant redundancies
across different channels, allowing for the reconstruction of masked content
using cross-channel correlations. However, this assumption does not hold in
Multi-Channel Imaging (MCI), where channels may provide complementary
information with minimal feature overlap. Thus, these MAEs primarily learn
local structures within individual channels from patch reconstruction, failing
to fully leverage cross-channel interactions and limiting their MCI
effectiveness. In this paper, we present ChA-MAEViT, an MAE-based method that
enhances feature learning across MCI channels via four key strategies: (1)
dynamic channel-patch masking, which compels the model to reconstruct missing
channels in addition to masked patches, thereby enhancing cross-channel
dependencies and improving robustness to varying channel configurations; (2)
memory tokens, which serve as long-term memory aids to promote information
sharing across channels, addressing the challenges of reconstructing
structurally diverse channels; (3) hybrid token fusion module, which merges
fine-grained patch tokens with a global class token to capture richer
representations; and (4) Channel-Aware Decoder, a lightweight decoder utilizes
channel tokens to effectively reconstruct image patches. Experiments on
satellite and microscopy datasets, CHAMMI, JUMP-CP, and So2Sat, show that
ChA-MAEViT significantly outperforms state-of-the-art MCI-ViTs by 3.0-21.5%,
highlighting the importance of cross-channel interactions in MCI.
| no_new_dataset | 0.948632 |
2503.19332 | Zhiying Yan | Zhiying Yan, Yiyuan Liang, Shilv Cai, Tao Zhang, Sheng Zhong, Luxin
Yan, Xu Zou | Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D
Gaussian Spatting | ICME 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic 4D Gaussians can be used for reconstructing and understanding
dynamic scenes, with temporal variations than static scenes. Directly applying
static methods to understand dynamic scenes will fail to capture the temporal
features. Few works focus on dynamic scene understanding based on Gaussian
Splatting, since once the same update strategy is employed for both dynamic and
static parts, regardless of the distinction and interaction between Gaussians,
significant artifacts and noise appear. We propose Dual-Hierarchical
Optimization (DHO), which consists of Hierarchical Gaussian Flow and
Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former
implements effective division of static and dynamic rendering and features. The
latter helps to mitigate the issue of dynamic foreground rendering distortion
in textured complex scenes. Extensive experiments show that our method
consistently outperforms the baselines on both synthetic and real-world
datasets, and supports various downstream tasks. Project Page:
https://sweety-yan.github.io/DHO.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 03:46:13 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Yan",
"Zhiying",
""
],
[
"Liang",
"Yiyuan",
""
],
[
"Cai",
"Shilv",
""
],
[
"Zhang",
"Tao",
""
],
[
"Zhong",
"Sheng",
""
],
[
"Yan",
"Luxin",
""
],
[
"Zou",
"Xu",
""
]
] | TITLE: Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D
Gaussian Spatting
ABSTRACT: Semantic 4D Gaussians can be used for reconstructing and understanding
dynamic scenes, with temporal variations than static scenes. Directly applying
static methods to understand dynamic scenes will fail to capture the temporal
features. Few works focus on dynamic scene understanding based on Gaussian
Splatting, since once the same update strategy is employed for both dynamic and
static parts, regardless of the distinction and interaction between Gaussians,
significant artifacts and noise appear. We propose Dual-Hierarchical
Optimization (DHO), which consists of Hierarchical Gaussian Flow and
Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former
implements effective division of static and dynamic rendering and features. The
latter helps to mitigate the issue of dynamic foreground rendering distortion
in textured complex scenes. Extensive experiments show that our method
consistently outperforms the baselines on both synthetic and real-world
datasets, and supports various downstream tasks. Project Page:
https://sweety-yan.github.io/DHO.
| no_new_dataset | 0.953057 |
2503.19339 | Muhammad Shahbaz Khan | Amna Naeem, Muazzam A. Khan, Nada Alasbali, Jawad Ahmad, Aizaz Ahmad
Khattak, Muhammad Shahbaz Khan | Efficient IoT Intrusion Detection with an Improved Attention-Based
CNN-BiLSTM Architecture | null | null | null | null | cs.CR cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The ever-increasing security vulnerabilities in the Internet-of-Things (IoT)
systems require improved threat detection approaches. This paper presents a
compact and efficient approach to detect botnet attacks by employing an
integrated approach that consists of traffic pattern analysis, temporal support
learning, and focused feature extraction. The proposed attention-based model
benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification
accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while
maintaining high precision and recall across various scenarios. The proposed
model's performance is further validated by key parameters, such as Mathews
Correlation Coefficient and Cohen's kappa Correlation Coefficient. The
close-to-ideal results for these parameters demonstrate the proposed model's
ability to detect botnet attacks accurately and efficiently in practical
settings and on unseen data. The proposed model proved to be a powerful defense
mechanism for IoT networks to face emerging security challenges.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 04:12:14 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Naeem",
"Amna",
""
],
[
"Khan",
"Muazzam A.",
""
],
[
"Alasbali",
"Nada",
""
],
[
"Ahmad",
"Jawad",
""
],
[
"Khattak",
"Aizaz Ahmad",
""
],
[
"Khan",
"Muhammad Shahbaz",
""
]
] | TITLE: Efficient IoT Intrusion Detection with an Improved Attention-Based
CNN-BiLSTM Architecture
ABSTRACT: The ever-increasing security vulnerabilities in the Internet-of-Things (IoT)
systems require improved threat detection approaches. This paper presents a
compact and efficient approach to detect botnet attacks by employing an
integrated approach that consists of traffic pattern analysis, temporal support
learning, and focused feature extraction. The proposed attention-based model
benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification
accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while
maintaining high precision and recall across various scenarios. The proposed
model's performance is further validated by key parameters, such as Mathews
Correlation Coefficient and Cohen's kappa Correlation Coefficient. The
close-to-ideal results for these parameters demonstrate the proposed model's
ability to detect botnet attacks accurately and efficiently in practical
settings and on unseen data. The proposed model proved to be a powerful defense
mechanism for IoT networks to face emerging security challenges.
| no_new_dataset | 0.948202 |
2503.19356 | Reza Pourreza | Reza Pourreza, Rishit Dagli, Apratim Bhattacharyya, Sunny Panchal,
Guillaume Berger, Roland Memisevic | Can Vision-Language Models Answer Face to Face Questions in the
Real-World? | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | AI models have made significant strides in recent years in their ability to
describe and answer questions about real-world images. They have also made
progress in the ability to converse with users in real-time using audio input.
This raises the question: have we reached the point where AI models, connected
to a camera and microphone, can converse with users in real-time about scenes
and events that are unfolding live in front of the camera? This has been a
long-standing goal in AI and is a prerequisite for real-world AI assistants and
humanoid robots to interact with humans in everyday situations. In this work,
we introduce a new dataset and benchmark, the Qualcomm Interactive Video
Dataset (IVD), which allows us to assess the extent to which existing models
can support these abilities, and to what degree these capabilities can be
instilled through fine-tuning. The dataset is based on a simple
question-answering setup, where users ask questions that the system has to
answer, in real-time, based on the camera and audio input. We show that
existing models fall far behind human performance on this task, and we identify
the main sources for the performance gap. However, we also show that for many
of the required perceptual skills, fine-tuning on this form of data can
significantly reduce this gap.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 05:13:12 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Pourreza",
"Reza",
""
],
[
"Dagli",
"Rishit",
""
],
[
"Bhattacharyya",
"Apratim",
""
],
[
"Panchal",
"Sunny",
""
],
[
"Berger",
"Guillaume",
""
],
[
"Memisevic",
"Roland",
""
]
] | TITLE: Can Vision-Language Models Answer Face to Face Questions in the
Real-World?
ABSTRACT: AI models have made significant strides in recent years in their ability to
describe and answer questions about real-world images. They have also made
progress in the ability to converse with users in real-time using audio input.
This raises the question: have we reached the point where AI models, connected
to a camera and microphone, can converse with users in real-time about scenes
and events that are unfolding live in front of the camera? This has been a
long-standing goal in AI and is a prerequisite for real-world AI assistants and
humanoid robots to interact with humans in everyday situations. In this work,
we introduce a new dataset and benchmark, the Qualcomm Interactive Video
Dataset (IVD), which allows us to assess the extent to which existing models
can support these abilities, and to what degree these capabilities can be
instilled through fine-tuning. The dataset is based on a simple
question-answering setup, where users ask questions that the system has to
answer, in real-time, based on the camera and audio input. We show that
existing models fall far behind human performance on this task, and we identify
the main sources for the performance gap. However, we also show that for many
of the required perceptual skills, fine-tuning on this form of data can
significantly reduce this gap.
| new_dataset | 0.964489 |
2503.19358 | Zhiwei Huang | Zhiwei Huang, Hailin Yu, Yichun Shentu, Jin Yuan, Guofeng Zhang | From Sparse to Dense: Camera Relocalization with Scene-Specific Detector
from Feature Gaussian Splatting | 15 pages, 12 figures, CVPR 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel camera relocalization method, STDLoc, which
leverages Feature Gaussian as scene representation. STDLoc is a full
relocalization pipeline that can achieve accurate relocalization without
relying on any pose prior. Unlike previous coarse-to-fine localization methods
that require image retrieval first and then feature matching, we propose a
novel sparse-to-dense localization paradigm. Based on this scene
representation, we introduce a novel matching-oriented Gaussian sampling
strategy and a scene-specific detector to achieve efficient and robust initial
pose estimation. Furthermore, based on the initial localization results, we
align the query feature map to the Gaussian feature field by dense feature
matching to enable accurate localization. The experiments on indoor and outdoor
datasets show that STDLoc outperforms current state-of-the-art localization
methods in terms of localization accuracy and recall.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 05:18:19 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Huang",
"Zhiwei",
""
],
[
"Yu",
"Hailin",
""
],
[
"Shentu",
"Yichun",
""
],
[
"Yuan",
"Jin",
""
],
[
"Zhang",
"Guofeng",
""
]
] | TITLE: From Sparse to Dense: Camera Relocalization with Scene-Specific Detector
from Feature Gaussian Splatting
ABSTRACT: This paper presents a novel camera relocalization method, STDLoc, which
leverages Feature Gaussian as scene representation. STDLoc is a full
relocalization pipeline that can achieve accurate relocalization without
relying on any pose prior. Unlike previous coarse-to-fine localization methods
that require image retrieval first and then feature matching, we propose a
novel sparse-to-dense localization paradigm. Based on this scene
representation, we introduce a novel matching-oriented Gaussian sampling
strategy and a scene-specific detector to achieve efficient and robust initial
pose estimation. Furthermore, based on the initial localization results, we
align the query feature map to the Gaussian feature field by dense feature
matching to enable accurate localization. The experiments on indoor and outdoor
datasets show that STDLoc outperforms current state-of-the-art localization
methods in terms of localization accuracy and recall.
| no_new_dataset | 0.949295 |
2503.19359 | Yunhe Gao | Yunhe Gao, Di Liu, Zhuowei Li, Yunsheng Li, Dongdong Chen, Mu Zhou,
Dimitris N. Metaxas | Show and Segment: Universal Medical Image Segmentation via In-Context
Learning | CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Medical image segmentation remains challenging due to the vast diversity of
anatomical structures, imaging modalities, and segmentation tasks. While deep
learning has made significant advances, current approaches struggle to
generalize as they require task-specific training or fine-tuning on unseen
classes. We present Iris, a novel In-context Reference Image guided
Segmentation framework that enables flexible adaptation to novel tasks through
the use of reference examples without fine-tuning. At its core, Iris features a
lightweight context task encoding module that distills task-specific
information from reference context image-label pairs. This rich context
embedding information is used to guide the segmentation of target objects. By
decoupling task encoding from inference, Iris supports diverse strategies from
one-shot inference and context example ensemble to object-level context example
retrieval and in-context tuning. Through comprehensive evaluation across twelve
datasets, we demonstrate that Iris performs strongly compared to task-specific
models on in-distribution tasks. On seven held-out datasets, Iris shows
superior generalization to out-of-distribution data and unseen classes.
Further, Iris's task encoding module can automatically discover anatomical
relationships across datasets and modalities, offering insights into medical
objects without explicit anatomical supervision.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 05:26:10 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Gao",
"Yunhe",
""
],
[
"Liu",
"Di",
""
],
[
"Li",
"Zhuowei",
""
],
[
"Li",
"Yunsheng",
""
],
[
"Chen",
"Dongdong",
""
],
[
"Zhou",
"Mu",
""
],
[
"Metaxas",
"Dimitris N.",
""
]
] | TITLE: Show and Segment: Universal Medical Image Segmentation via In-Context
Learning
ABSTRACT: Medical image segmentation remains challenging due to the vast diversity of
anatomical structures, imaging modalities, and segmentation tasks. While deep
learning has made significant advances, current approaches struggle to
generalize as they require task-specific training or fine-tuning on unseen
classes. We present Iris, a novel In-context Reference Image guided
Segmentation framework that enables flexible adaptation to novel tasks through
the use of reference examples without fine-tuning. At its core, Iris features a
lightweight context task encoding module that distills task-specific
information from reference context image-label pairs. This rich context
embedding information is used to guide the segmentation of target objects. By
decoupling task encoding from inference, Iris supports diverse strategies from
one-shot inference and context example ensemble to object-level context example
retrieval and in-context tuning. Through comprehensive evaluation across twelve
datasets, we demonstrate that Iris performs strongly compared to task-specific
models on in-distribution tasks. On seven held-out datasets, Iris shows
superior generalization to out-of-distribution data and unseen classes.
Further, Iris's task encoding module can automatically discover anatomical
relationships across datasets and modalities, offering insights into medical
objects without explicit anatomical supervision.
| no_new_dataset | 0.940953 |
2503.19361 | Piera Riccio | Piera Riccio, Francesco Galati, Kajetan Schweighofer, Noa Garcia,
Nuria Oliver | ImageSet2Text: Describing Sets of Images through Text | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce ImageSet2Text, a novel approach that leverages vision-language
foundation models to automatically create natural language descriptions of
image sets. Inspired by concept bottleneck models (CBMs) and based on
visual-question answering (VQA) chains, ImageSet2Text iteratively extracts key
concepts from image subsets, encodes them into a structured graph, and refines
insights using an external knowledge graph and CLIP-based validation. This
iterative process enhances interpretability and enables accurate and detailed
set-level summarization. Through extensive experiments, we evaluate
ImageSet2Text's descriptions on accuracy, completeness, readability and overall
quality, benchmarking it against existing vision-language models and
introducing new datasets for large-scale group image captioning.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 05:29:50 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Riccio",
"Piera",
""
],
[
"Galati",
"Francesco",
""
],
[
"Schweighofer",
"Kajetan",
""
],
[
"Garcia",
"Noa",
""
],
[
"Oliver",
"Nuria",
""
]
] | TITLE: ImageSet2Text: Describing Sets of Images through Text
ABSTRACT: We introduce ImageSet2Text, a novel approach that leverages vision-language
foundation models to automatically create natural language descriptions of
image sets. Inspired by concept bottleneck models (CBMs) and based on
visual-question answering (VQA) chains, ImageSet2Text iteratively extracts key
concepts from image subsets, encodes them into a structured graph, and refines
insights using an external knowledge graph and CLIP-based validation. This
iterative process enhances interpretability and enables accurate and detailed
set-level summarization. Through extensive experiments, we evaluate
ImageSet2Text's descriptions on accuracy, completeness, readability and overall
quality, benchmarking it against existing vision-language models and
introducing new datasets for large-scale group image captioning.
| new_dataset | 0.951504 |
2503.19370 | Taishin Saito | Taishin Saito | A Benign Activity Extraction Method for Malignant Activity
Identification using Data Provenance | master's thesis | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | In order to understand the overall picture of cyber attacks and to identify
the source of cyber attacks, a method to identify malicious activities by
automatically creating a graph that ties together the dependencies of a series
of related events by tracking Data Provenance has been developed. However, the
problem of dependency explosion, in which a large number of normal computer
system operations such as operations by authorized users are included in the
dependencies, results in a huge generated graph, making it difficult to
identify malicious activities.
In this paper, we propose a method to reduce the search space for malicious
activities by extracting and removing frequently occurring benign activities
through natural language processing of log data and analysis of activities in
the computer system using similarity judgments. In the evaluation experiment,
we used the DARPA TC Dateset, a large-scale public dataset, to evaluate the
effectiveness of the proposed method on the dependency explosion problem. In
addition, we showed that about 6.8 to 39% of the activities in a computer
system could be defined as patterns of benign activities. In addition, we
showed that removing benign activities extracted from a portion of the log data
(approximately 1.4% to 3.2% in size) can significantly reduce the search space
(up to approximately 52%) in large data sets.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 05:52:41 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Saito",
"Taishin",
""
]
] | TITLE: A Benign Activity Extraction Method for Malignant Activity
Identification using Data Provenance
ABSTRACT: In order to understand the overall picture of cyber attacks and to identify
the source of cyber attacks, a method to identify malicious activities by
automatically creating a graph that ties together the dependencies of a series
of related events by tracking Data Provenance has been developed. However, the
problem of dependency explosion, in which a large number of normal computer
system operations such as operations by authorized users are included in the
dependencies, results in a huge generated graph, making it difficult to
identify malicious activities.
In this paper, we propose a method to reduce the search space for malicious
activities by extracting and removing frequently occurring benign activities
through natural language processing of log data and analysis of activities in
the computer system using similarity judgments. In the evaluation experiment,
we used the DARPA TC Dateset, a large-scale public dataset, to evaluate the
effectiveness of the proposed method on the dependency explosion problem. In
addition, we showed that about 6.8 to 39% of the activities in a computer
system could be defined as patterns of benign activities. In addition, we
showed that removing benign activities extracted from a portion of the log data
(approximately 1.4% to 3.2% in size) can significantly reduce the search space
(up to approximately 52%) in large data sets.
| no_new_dataset | 0.951414 |
2503.19377 | Akshay Kulkarni | Akshay Kulkarni, Ge Yan, Chung-En Sun, Tuomas Oikarinen, Tsui-Wei Weng | Interpretable Generative Models through Post-hoc Concept Bottlenecks | CVPR 2025. Project Page:
https://lilywenglab.github.io/posthoc-generative-cbm/ | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Concept bottleneck models (CBM) aim to produce inherently interpretable
models that rely on human-understandable concepts for their predictions.
However, existing approaches to design interpretable generative models based on
CBMs are not yet efficient and scalable, as they require expensive generative
model training from scratch as well as real images with labor-intensive concept
supervision. To address these challenges, we present two novel and low-cost
methods to build interpretable generative models through post-hoc techniques
and we name our approaches: concept-bottleneck autoencoder (CB-AE) and concept
controller (CC). Our proposed approaches enable efficient and scalable training
without the need of real data and require only minimal to no concept
supervision. Additionally, our methods generalize across modern generative
model families including generative adversarial networks and diffusion models.
We demonstrate the superior interpretability and steerability of our methods on
numerous standard datasets like CelebA, CelebA-HQ, and CUB with large
improvements (average ~25%) over the prior work, while being 4-15x faster to
train. Finally, a large-scale user study is performed to validate the
interpretability and steerability of our methods.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 06:09:51 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Kulkarni",
"Akshay",
""
],
[
"Yan",
"Ge",
""
],
[
"Sun",
"Chung-En",
""
],
[
"Oikarinen",
"Tuomas",
""
],
[
"Weng",
"Tsui-Wei",
""
]
] | TITLE: Interpretable Generative Models through Post-hoc Concept Bottlenecks
ABSTRACT: Concept bottleneck models (CBM) aim to produce inherently interpretable
models that rely on human-understandable concepts for their predictions.
However, existing approaches to design interpretable generative models based on
CBMs are not yet efficient and scalable, as they require expensive generative
model training from scratch as well as real images with labor-intensive concept
supervision. To address these challenges, we present two novel and low-cost
methods to build interpretable generative models through post-hoc techniques
and we name our approaches: concept-bottleneck autoencoder (CB-AE) and concept
controller (CC). Our proposed approaches enable efficient and scalable training
without the need of real data and require only minimal to no concept
supervision. Additionally, our methods generalize across modern generative
model families including generative adversarial networks and diffusion models.
We demonstrate the superior interpretability and steerability of our methods on
numerous standard datasets like CelebA, CelebA-HQ, and CUB with large
improvements (average ~25%) over the prior work, while being 4-15x faster to
train. Finally, a large-scale user study is performed to validate the
interpretability and steerability of our methods.
| no_new_dataset | 0.94428 |
2503.19380 | Yiwei Zhang | Yiwei Zhang | Social Network User Profiling for Anomaly Detection Based on Graph
Neural Networks | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study proposes a risk pricing anomaly detection method for social
network user portraits based on graph neural networks (GNNs), aiming to improve
the ability to identify abnormal users in social network environments. In view
of the limitations of traditional methods in social network data modeling, this
paper combines graph autoencoders (GAEs) and graph attention networks (GATs) to
achieve accurate detection of abnormal users through dynamic aggregation of
neighbor features and reconstruction error evaluation. The Facebook Page-Page
Network dataset is used in the experiment and compared with VAE, GNN,
Transformer and GAE. The results show that the proposed method achieves the
best performance in AUC, F1-score, Precision and Recall, verifying its
effectiveness. In addition, this paper explores the computational efficiency of
the model in large-scale data and looks forward to combining self-supervised
learning, federated learning, and other technologies in the future to improve
the robustness and privacy protection of risk assessment. The research results
can provide efficient anomaly detection solutions for financial risk control,
social security management, and other fields.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 06:16:17 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhang",
"Yiwei",
""
]
] | TITLE: Social Network User Profiling for Anomaly Detection Based on Graph
Neural Networks
ABSTRACT: This study proposes a risk pricing anomaly detection method for social
network user portraits based on graph neural networks (GNNs), aiming to improve
the ability to identify abnormal users in social network environments. In view
of the limitations of traditional methods in social network data modeling, this
paper combines graph autoencoders (GAEs) and graph attention networks (GATs) to
achieve accurate detection of abnormal users through dynamic aggregation of
neighbor features and reconstruction error evaluation. The Facebook Page-Page
Network dataset is used in the experiment and compared with VAE, GNN,
Transformer and GAE. The results show that the proposed method achieves the
best performance in AUC, F1-score, Precision and Recall, verifying its
effectiveness. In addition, this paper explores the computational efficiency of
the model in large-scale data and looks forward to combining self-supervised
learning, federated learning, and other technologies in the future to improve
the robustness and privacy protection of risk assessment. The research results
can provide efficient anomaly detection solutions for financial risk control,
social security management, and other fields.
| no_new_dataset | 0.947284 |
2503.19382 | Haifeng Li | Yuhan Wang, Silu He, Qinyao Luo, Hongyuan Yuan, Ling Zhao, Jiawei Zhu,
Haifeng Li | Causal invariant geographic network representations with feature and
structural distribution shifts | 15 pages, 3 figures, 8 tables | Future Generation Computer Systems 2025 | 10.1016/j.future.2025.107814 | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The existing methods learn geographic network representations through deep
graph neural networks (GNNs) based on the i.i.d. assumption. However, the
spatial heterogeneity and temporal dynamics of geographic data make the
out-of-distribution (OOD) generalisation problem particularly salient. The
latter are particularly sensitive to distribution shifts (feature and
structural shifts) between testing and training data and are the main causes of
the OOD generalisation problem. Spurious correlations are present between
invariant and background representations due to selection biases and
environmental effects, resulting in the model extremes being more likely to
learn background representations. The existing approaches focus on background
representation changes that are determined by shifts in the feature
distributions of nodes in the training and test data while ignoring changes in
the proportional distributions of heterogeneous and homogeneous neighbour
nodes, which we refer to as structural distribution shifts. We propose a
feature-structure mixed invariant representation learning (FSM-IRL) model that
accounts for both feature distribution shifts and structural distribution
shifts. To address structural distribution shifts, we introduce a sampling
method based on causal attention, encouraging the model to identify nodes
possessing strong causal relationships with labels or nodes that are more
similar to the target node. Inspired by the Hilbert-Schmidt independence
criterion, we implement a reweighting strategy to maximise the orthogonality of
the node representations, thereby mitigating the spurious correlations among
the node representations and suppressing the learning of background
representations. Our experiments demonstrate that FSM-IRL exhibits strong
learning capabilities on both geographic and social network datasets in OOD
scenarios.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 06:21:57 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Wang",
"Yuhan",
""
],
[
"He",
"Silu",
""
],
[
"Luo",
"Qinyao",
""
],
[
"Yuan",
"Hongyuan",
""
],
[
"Zhao",
"Ling",
""
],
[
"Zhu",
"Jiawei",
""
],
[
"Li",
"Haifeng",
""
]
] | TITLE: Causal invariant geographic network representations with feature and
structural distribution shifts
ABSTRACT: The existing methods learn geographic network representations through deep
graph neural networks (GNNs) based on the i.i.d. assumption. However, the
spatial heterogeneity and temporal dynamics of geographic data make the
out-of-distribution (OOD) generalisation problem particularly salient. The
latter are particularly sensitive to distribution shifts (feature and
structural shifts) between testing and training data and are the main causes of
the OOD generalisation problem. Spurious correlations are present between
invariant and background representations due to selection biases and
environmental effects, resulting in the model extremes being more likely to
learn background representations. The existing approaches focus on background
representation changes that are determined by shifts in the feature
distributions of nodes in the training and test data while ignoring changes in
the proportional distributions of heterogeneous and homogeneous neighbour
nodes, which we refer to as structural distribution shifts. We propose a
feature-structure mixed invariant representation learning (FSM-IRL) model that
accounts for both feature distribution shifts and structural distribution
shifts. To address structural distribution shifts, we introduce a sampling
method based on causal attention, encouraging the model to identify nodes
possessing strong causal relationships with labels or nodes that are more
similar to the target node. Inspired by the Hilbert-Schmidt independence
criterion, we implement a reweighting strategy to maximise the orthogonality of
the node representations, thereby mitigating the spurious correlations among
the node representations and suppressing the learning of background
representations. Our experiments demonstrate that FSM-IRL exhibits strong
learning capabilities on both geographic and social network datasets in OOD
scenarios.
| no_new_dataset | 0.951818 |
2503.19391 | Zhiying Song | Zhiying Song, Lei Yang, Fuxi Wen and Jun Li | TraF-Align: Trajectory-aware Feature Alignment for Asynchronous
Multi-agent Perception | Accepted to CVPR 2025 | null | null | null | cs.CV cs.MA | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cooperative perception presents significant potential for enhancing the
sensing capabilities of individual vehicles, however, inter-agent latency
remains a critical challenge. Latencies cause misalignments in both spatial and
semantic features, complicating the fusion of real-time observations from the
ego vehicle with delayed data from others. To address these issues, we propose
TraF-Align, a novel framework that learns the flow path of features by
predicting the feature-level trajectory of objects from past observations up to
the ego vehicle's current time. By generating temporally ordered sampling
points along these paths, TraF-Align directs attention from the current-time
query to relevant historical features along each trajectory, supporting the
reconstruction of current-time features and promoting semantic interaction
across multiple frames. This approach corrects spatial misalignment and ensures
semantic consistency across agents, effectively compensating for motion and
achieving coherent feature fusion. Experiments on two real-world datasets,
V2V4Real and DAIR-V2X-Seq, show that TraF-Align sets a new benchmark for
asynchronous cooperative perception.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 06:56:35 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Song",
"Zhiying",
""
],
[
"Yang",
"Lei",
""
],
[
"Wen",
"Fuxi",
""
],
[
"Li",
"Jun",
""
]
] | TITLE: TraF-Align: Trajectory-aware Feature Alignment for Asynchronous
Multi-agent Perception
ABSTRACT: Cooperative perception presents significant potential for enhancing the
sensing capabilities of individual vehicles, however, inter-agent latency
remains a critical challenge. Latencies cause misalignments in both spatial and
semantic features, complicating the fusion of real-time observations from the
ego vehicle with delayed data from others. To address these issues, we propose
TraF-Align, a novel framework that learns the flow path of features by
predicting the feature-level trajectory of objects from past observations up to
the ego vehicle's current time. By generating temporally ordered sampling
points along these paths, TraF-Align directs attention from the current-time
query to relevant historical features along each trajectory, supporting the
reconstruction of current-time features and promoting semantic interaction
across multiple frames. This approach corrects spatial misalignment and ensures
semantic consistency across agents, effectively compensating for motion and
achieving coherent feature fusion. Experiments on two real-world datasets,
V2V4Real and DAIR-V2X-Seq, show that TraF-Align sets a new benchmark for
asynchronous cooperative perception.
| no_new_dataset | 0.94887 |
2503.19397 | Chenghao Li | Chenghao Li, Razvan Beuran, Nak Young Chong | Quality-focused Active Adversarial Policy for Safe Grasping in
Human-Robot Interaction | null | null | null | null | cs.RO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Vision-guided robot grasping methods based on Deep Neural Networks (DNNs)
have achieved remarkable success in handling unknown objects, attributable to
their powerful generalizability. However, these methods with this
generalizability tend to recognize the human hand and its adjacent objects as
graspable targets, compromising safety during Human-Robot Interaction (HRI). In
this work, we propose the Quality-focused Active Adversarial Policy (QFAAP) to
solve this problem. Specifically, the first part is the Adversarial Quality
Patch (AQP), wherein we design the adversarial quality patch loss and leverage
the grasp dataset to optimize a patch with high quality scores. Next, we
construct the Projected Quality Gradient Descent (PQGD) and integrate it with
the AQP, which contains only the hand region within each real-time frame,
endowing the AQP with fast adaptability to the human hand shape. Through AQP
and PQGD, the hand can be actively adversarial with the surrounding objects,
lowering their quality scores. Therefore, further setting the quality score of
the hand to zero will reduce the grasping priority of both the hand and its
adjacent objects, enabling the robot to grasp other objects away from the hand
without emergency stops. We conduct extensive experiments on the benchmark
datasets and a cobot, showing the effectiveness of QFAAP. Our code and demo
videos are available here: https://github.com/clee-jaist/QFAAP.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 07:09:31 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Li",
"Chenghao",
""
],
[
"Beuran",
"Razvan",
""
],
[
"Chong",
"Nak Young",
""
]
] | TITLE: Quality-focused Active Adversarial Policy for Safe Grasping in
Human-Robot Interaction
ABSTRACT: Vision-guided robot grasping methods based on Deep Neural Networks (DNNs)
have achieved remarkable success in handling unknown objects, attributable to
their powerful generalizability. However, these methods with this
generalizability tend to recognize the human hand and its adjacent objects as
graspable targets, compromising safety during Human-Robot Interaction (HRI). In
this work, we propose the Quality-focused Active Adversarial Policy (QFAAP) to
solve this problem. Specifically, the first part is the Adversarial Quality
Patch (AQP), wherein we design the adversarial quality patch loss and leverage
the grasp dataset to optimize a patch with high quality scores. Next, we
construct the Projected Quality Gradient Descent (PQGD) and integrate it with
the AQP, which contains only the hand region within each real-time frame,
endowing the AQP with fast adaptability to the human hand shape. Through AQP
and PQGD, the hand can be actively adversarial with the surrounding objects,
lowering their quality scores. Therefore, further setting the quality score of
the hand to zero will reduce the grasping priority of both the hand and its
adjacent objects, enabling the robot to grasp other objects away from the hand
without emergency stops. We conduct extensive experiments on the benchmark
datasets and a cobot, showing the effectiveness of QFAAP. Our code and demo
videos are available here: https://github.com/clee-jaist/QFAAP.
| no_new_dataset | 0.948728 |
2503.19405 | Mingxiao Tu | Mingxiao Tu, Hoijoon Jung, Alireza Moghadam, Jineel Raythatha, Lachlan
Allan, Jeremy Hsu, Andre Kyme, Jinman Kim | Multi-modal 3D Pose and Shape Estimation with Computed Tomography | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In perioperative care, precise in-bed 3D patient pose and shape estimation
(PSE) can be vital in optimizing patient positioning in preoperative planning,
enabling accurate overlay of medical images for augmented reality-based
surgical navigation, and mitigating risks of prolonged immobility during
recovery. Conventional PSE methods relying on modalities such as RGB-D,
infrared, or pressure maps often struggle with occlusions caused by bedding and
complex patient positioning, leading to inaccurate estimation that can affect
clinical outcomes. To address these challenges, we present the first
multi-modal in-bed patient 3D PSE network that fuses detailed geometric
features extracted from routinely acquired computed tomography (CT) scans with
depth maps (mPSE-CT). mPSE-CT incorporates a shape estimation module that
utilizes probabilistic correspondence alignment, a pose estimation module with
a refined neural network, and a final parameters mixing module. This
multi-modal network robustly reconstructs occluded body regions and enhances
the accuracy of the estimated 3D human mesh model. We validated mPSE-CT using
proprietary whole-body rigid phantom and volunteer datasets in clinical
scenarios. mPSE-CT outperformed the best-performing prior method by 23% and
49.16% in pose and shape estimation respectively, demonstrating its potential
for improving clinical outcomes in challenging perioperative environments.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 07:24:58 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Tu",
"Mingxiao",
""
],
[
"Jung",
"Hoijoon",
""
],
[
"Moghadam",
"Alireza",
""
],
[
"Raythatha",
"Jineel",
""
],
[
"Allan",
"Lachlan",
""
],
[
"Hsu",
"Jeremy",
""
],
[
"Kyme",
"Andre",
""
],
[
"Kim",
"Jinman",
""
]
] | TITLE: Multi-modal 3D Pose and Shape Estimation with Computed Tomography
ABSTRACT: In perioperative care, precise in-bed 3D patient pose and shape estimation
(PSE) can be vital in optimizing patient positioning in preoperative planning,
enabling accurate overlay of medical images for augmented reality-based
surgical navigation, and mitigating risks of prolonged immobility during
recovery. Conventional PSE methods relying on modalities such as RGB-D,
infrared, or pressure maps often struggle with occlusions caused by bedding and
complex patient positioning, leading to inaccurate estimation that can affect
clinical outcomes. To address these challenges, we present the first
multi-modal in-bed patient 3D PSE network that fuses detailed geometric
features extracted from routinely acquired computed tomography (CT) scans with
depth maps (mPSE-CT). mPSE-CT incorporates a shape estimation module that
utilizes probabilistic correspondence alignment, a pose estimation module with
a refined neural network, and a final parameters mixing module. This
multi-modal network robustly reconstructs occluded body regions and enhances
the accuracy of the estimated 3D human mesh model. We validated mPSE-CT using
proprietary whole-body rigid phantom and volunteer datasets in clinical
scenarios. mPSE-CT outperformed the best-performing prior method by 23% and
49.16% in pose and shape estimation respectively, demonstrating its potential
for improving clinical outcomes in challenging perioperative environments.
| no_new_dataset | 0.950457 |
2503.19407 | Bingjian Yao | Bingjian Yao, Weiping Lin, Yan He, Zheng Wang, Liangsheng Wang | A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide
Images | 10 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fine-grained annotations in whole slide images (WSIs) show the boundaries
of various pathological regions. However, generating such detailed annotation
is often costly, whereas the coarse annotations are relatively simpler to
produce. Existing methods for refining coarse annotations often rely on
extensive training samples or clean datasets, and fail to capture both
intra-slide and inter-slide latent sematic patterns, limiting their precision.
In this paper, we propose a prototype-guided approach. Specifically, we
introduce a local-to-global approach to construct non-redundant representative
prototypes by jointly modeling intra-slide local semantics and inter-slide
contextual relationships. Then a prototype-guided pseudo-labeling module is
proposed for refining coarse annotations. Finally, we employ dynamic data
sampling and re-finetuning strategy to train a patch classifier. Extensive
experiments on three publicly available WSI datasets, covering lymph, liver,
and colorectal cancers, demonstrate that our method significantly outperforms
existing state-of-the-art (SOTA) methods. The code will be available.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 07:34:06 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Yao",
"Bingjian",
""
],
[
"Lin",
"Weiping",
""
],
[
"He",
"Yan",
""
],
[
"Wang",
"Zheng",
""
],
[
"Wang",
"Liangsheng",
""
]
] | TITLE: A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide
Images
ABSTRACT: The fine-grained annotations in whole slide images (WSIs) show the boundaries
of various pathological regions. However, generating such detailed annotation
is often costly, whereas the coarse annotations are relatively simpler to
produce. Existing methods for refining coarse annotations often rely on
extensive training samples or clean datasets, and fail to capture both
intra-slide and inter-slide latent sematic patterns, limiting their precision.
In this paper, we propose a prototype-guided approach. Specifically, we
introduce a local-to-global approach to construct non-redundant representative
prototypes by jointly modeling intra-slide local semantics and inter-slide
contextual relationships. Then a prototype-guided pseudo-labeling module is
proposed for refining coarse annotations. Finally, we employ dynamic data
sampling and re-finetuning strategy to train a patch classifier. Extensive
experiments on three publicly available WSI datasets, covering lymph, liver,
and colorectal cancers, demonstrate that our method significantly outperforms
existing state-of-the-art (SOTA) methods. The code will be available.
| no_new_dataset | 0.946448 |
2503.19423 | Ling Xiao | Tingting Diao, Xinzhang Wu, Lina Yang, Ling Xiao, Yunxuan Dong | A novel forecasting framework combining virtual samples and enhanced
Transformer models for tourism demand forecasting | null | null | null | null | stat.AP cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate tourism demand forecasting is hindered by limited historical data
and complex spatiotemporal dependencies among tourist origins. A novel
forecasting framework integrating virtual sample generation and a novel
Transformer predictor addresses constraints arising from restricted data
availability. A spatiotemporal GAN produces realistic virtual samples by
dynamically modeling spatial correlations through a graph convolutional
network, and an enhanced Transformer captures local patterns with causal
convolutions and long-term dependencies with self-attention,eliminating
autoregressive decoding. A joint training strategy refines virtual sample
generation based on predictor feedback to maintain robust performance under
data-scarce conditions. Experimental evaluations on real-world daily and
monthly tourism demand datasets indicate a reduction in average MASE by 18.37%
compared to conventional Transformer-based models, demonstrating improved
forecasting accuracy. The integration of adaptive spatiotemporal sample
augmentation with a specialized Transformer can effectively address
limited-data forecasting scenarios in tourism management.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:02:09 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Diao",
"Tingting",
""
],
[
"Wu",
"Xinzhang",
""
],
[
"Yang",
"Lina",
""
],
[
"Xiao",
"Ling",
""
],
[
"Dong",
"Yunxuan",
""
]
] | TITLE: A novel forecasting framework combining virtual samples and enhanced
Transformer models for tourism demand forecasting
ABSTRACT: Accurate tourism demand forecasting is hindered by limited historical data
and complex spatiotemporal dependencies among tourist origins. A novel
forecasting framework integrating virtual sample generation and a novel
Transformer predictor addresses constraints arising from restricted data
availability. A spatiotemporal GAN produces realistic virtual samples by
dynamically modeling spatial correlations through a graph convolutional
network, and an enhanced Transformer captures local patterns with causal
convolutions and long-term dependencies with self-attention,eliminating
autoregressive decoding. A joint training strategy refines virtual sample
generation based on predictor feedback to maintain robust performance under
data-scarce conditions. Experimental evaluations on real-world daily and
monthly tourism demand datasets indicate a reduction in average MASE by 18.37%
compared to conventional Transformer-based models, demonstrating improved
forecasting accuracy. The integration of adaptive spatiotemporal sample
augmentation with a specialized Transformer can effectively address
limited-data forecasting scenarios in tourism management.
| no_new_dataset | 0.945096 |
2503.19425 | Yue Yin | Yue Yin, Hai Xiao | Oxidation States in Solids from Data-Driven Paradigms | null | null | null | null | physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The oxidation state (OS) is an essential chemical concept that embodies
chemical intuition but cannot be computed with well-defined physical laws. We
establish a data-driven paradigm, with its implementation as Tsinghua Oxidation
States in Solids (TOSS), to explicitly compute the OSs in crystal structures as
the emergent properties from large-sized datasets based on Bayesian maximum a
posteriori probability (MAP). TOSS employs two looping structures over the
large-sized dataset of crystal structures to obtain an emergent library of
distance distributions as the foundation for chemically intuitive understanding
and then determine the OSs by minimizing a loss function for each structure
based on MAP and distance distributions in the whole dataset. The application
of TOSS to a dataset of $\mathrm{>}$1,000,000 crystal structures delivers a
superior success rate, and using the resulting OSs as the dataset, we further
train a data-driven alternative to TOSS based on graph convolutional networks.
We expect TOSS and the ML-model-based alternative to find a wide spectrum of
applications, and this work also demonstrates an encouraging example for the
data-driven paradigms to explicitly compute the chemical intuition for tackling
complex problems in chemistry.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:05:55 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Yin",
"Yue",
""
],
[
"Xiao",
"Hai",
""
]
] | TITLE: Oxidation States in Solids from Data-Driven Paradigms
ABSTRACT: The oxidation state (OS) is an essential chemical concept that embodies
chemical intuition but cannot be computed with well-defined physical laws. We
establish a data-driven paradigm, with its implementation as Tsinghua Oxidation
States in Solids (TOSS), to explicitly compute the OSs in crystal structures as
the emergent properties from large-sized datasets based on Bayesian maximum a
posteriori probability (MAP). TOSS employs two looping structures over the
large-sized dataset of crystal structures to obtain an emergent library of
distance distributions as the foundation for chemically intuitive understanding
and then determine the OSs by minimizing a loss function for each structure
based on MAP and distance distributions in the whole dataset. The application
of TOSS to a dataset of $\mathrm{>}$1,000,000 crystal structures delivers a
superior success rate, and using the resulting OSs as the dataset, we further
train a data-driven alternative to TOSS based on graph convolutional networks.
We expect TOSS and the ML-model-based alternative to find a wide spectrum of
applications, and this work also demonstrates an encouraging example for the
data-driven paradigms to explicitly compute the chemical intuition for tackling
complex problems in chemistry.
| no_new_dataset | 0.939359 |
2503.19427 | Muyi Bao | Muyi Bao, Shuchang Lyu, Zhaoyang Xu, Qi Zhao, Changyu Zeng, Wenpei Bai
and Guangliang Cheng | ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion
Segmentation | null | null | null | null | eess.IV cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Skin lesion segmentation is a critical challenge in computer vision, and it
is essential to separate pathological features from healthy skin for
diagnostics accurately. Traditional Convolutional Neural Networks (CNNs) are
limited by narrow receptive fields, and Transformers face significant
computational burdens. This paper presents a novel skin lesion segmentation
framework, the Atrous Shifted Parallel Vision Mamba UNet (ASP-VMUNet), which
integrates the efficient and scalable Mamba architecture to overcome
limitations in traditional CNNs and computationally demanding Transformers. The
framework introduces an atrous scan technique that minimizes background
interference and expands the receptive field, enhancing Mamba's scanning
capabilities. Additionally, the inclusion of a Parallel Vision Mamba (PVM)
layer and a shift round operation optimizes feature segmentation and fosters
rich inter-segment information exchange. A supplementary CNN branch with a
Selective-Kernel (SK) Block further refines the segmentation by blending local
and global contextual information. Tested on four benchmark datasets
(ISIC16/17/18 and PH2), ASP-VMUNet demonstrates superior performance in skin
lesion segmentation, validated by comprehensive ablation studies. This approach
not only advances medical image segmentation but also highlights the benefits
of hybrid architectures in medical imaging technology. Our code is available at
https://github.com/BaoBao0926/ASP-VMUNet/tree/main.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:17:22 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Bao",
"Muyi",
""
],
[
"Lyu",
"Shuchang",
""
],
[
"Xu",
"Zhaoyang",
""
],
[
"Zhao",
"Qi",
""
],
[
"Zeng",
"Changyu",
""
],
[
"Bai",
"Wenpei",
""
],
[
"Cheng",
"Guangliang",
""
]
] | TITLE: ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion
Segmentation
ABSTRACT: Skin lesion segmentation is a critical challenge in computer vision, and it
is essential to separate pathological features from healthy skin for
diagnostics accurately. Traditional Convolutional Neural Networks (CNNs) are
limited by narrow receptive fields, and Transformers face significant
computational burdens. This paper presents a novel skin lesion segmentation
framework, the Atrous Shifted Parallel Vision Mamba UNet (ASP-VMUNet), which
integrates the efficient and scalable Mamba architecture to overcome
limitations in traditional CNNs and computationally demanding Transformers. The
framework introduces an atrous scan technique that minimizes background
interference and expands the receptive field, enhancing Mamba's scanning
capabilities. Additionally, the inclusion of a Parallel Vision Mamba (PVM)
layer and a shift round operation optimizes feature segmentation and fosters
rich inter-segment information exchange. A supplementary CNN branch with a
Selective-Kernel (SK) Block further refines the segmentation by blending local
and global contextual information. Tested on four benchmark datasets
(ISIC16/17/18 and PH2), ASP-VMUNet demonstrates superior performance in skin
lesion segmentation, validated by comprehensive ablation studies. This approach
not only advances medical image segmentation but also highlights the benefits
of hybrid architectures in medical imaging technology. Our code is available at
https://github.com/BaoBao0926/ASP-VMUNet/tree/main.
| no_new_dataset | 0.951818 |
2503.19445 | Yue Yin | Yue Yin, Jiangshan He, Hai Xiao | LOCAL: A Graph-Based Active Learning Approach for Stability Analysis of
DAC@NG Catalysts | null | null | null | null | physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dual atomic catalysts supported by nitrogen-doped graphene (DAC@NG) offer
significant potential in catalytic applications by overcoming intrinsic
limitations associated with single atomic catalysts. However, accurately
determining their stability and atomic-scale configurations remains
computationally challenging due to extensive structural variability. In this
study, we present the LOCalization and Active Learning (LOCAL) framework, an
innovative, scalable approach employing two graph convolutional network (GCN)
models (POS2COHP and Graph2E) to predict stability energies directly from
initial DAC@NG structures. Leveraging an extensive dataset of 611,648 DAC@NG
structures, encompassing 38 metal elements, six distinct graphene
quadra-vacancy patterns, and diverse carbon/nitrogen coordination environments,
LOCAL achieved a remarkable validation mean absolute error of just 0.145 eV.
Utilizing this framework, we systematically analyzed stability trends across
various metal pairs, successfully generating phase diagrams for experimentally
validated bimetallic systems (Co-Ni, Fe-Ni, Fe-Mn, and Ag-Ni). These results
underscore LOCAL's capability for rapidly evaluating structural stability,
significantly accelerating the discovery and optimization of high-performance
catalysts. The developed dataset and LOCAL framework are publicly available,
offering a valuable resource for future catalyst design and broader exploration
of catalytic materials.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:36:07 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Yin",
"Yue",
""
],
[
"He",
"Jiangshan",
""
],
[
"Xiao",
"Hai",
""
]
] | TITLE: LOCAL: A Graph-Based Active Learning Approach for Stability Analysis of
DAC@NG Catalysts
ABSTRACT: Dual atomic catalysts supported by nitrogen-doped graphene (DAC@NG) offer
significant potential in catalytic applications by overcoming intrinsic
limitations associated with single atomic catalysts. However, accurately
determining their stability and atomic-scale configurations remains
computationally challenging due to extensive structural variability. In this
study, we present the LOCalization and Active Learning (LOCAL) framework, an
innovative, scalable approach employing two graph convolutional network (GCN)
models (POS2COHP and Graph2E) to predict stability energies directly from
initial DAC@NG structures. Leveraging an extensive dataset of 611,648 DAC@NG
structures, encompassing 38 metal elements, six distinct graphene
quadra-vacancy patterns, and diverse carbon/nitrogen coordination environments,
LOCAL achieved a remarkable validation mean absolute error of just 0.145 eV.
Utilizing this framework, we systematically analyzed stability trends across
various metal pairs, successfully generating phase diagrams for experimentally
validated bimetallic systems (Co-Ni, Fe-Ni, Fe-Mn, and Ag-Ni). These results
underscore LOCAL's capability for rapidly evaluating structural stability,
significantly accelerating the discovery and optimization of high-performance
catalysts. The developed dataset and LOCAL framework are publicly available,
offering a valuable resource for future catalyst design and broader exploration
of catalytic materials.
| no_new_dataset | 0.801276 |
2503.19452 | Yiqing Li | Yiqing Li, Xuan Wang, Jiawei Wu, Yikun Ma, Zhi Jin | SparseGS-W: Sparse-View 3D Gaussian Splatting in the Wild with
Generative Priors | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthesizing novel views of large-scale scenes from unconstrained in-the-wild
images is an important but challenging task in computer vision. Existing
methods, which optimize per-image appearance and transient occlusion through
implicit neural networks from dense training views (approximately 1000 images),
struggle to perform effectively under sparse input conditions, resulting in
noticeable artifacts. To this end, we propose SparseGS-W, a novel framework
based on 3D Gaussian Splatting that enables the reconstruction of complex
outdoor scenes and handles occlusions and appearance changes with as few as
five training images. We leverage geometric priors and constrained diffusion
priors to compensate for the lack of multi-view information from extremely
sparse input. Specifically, we propose a plug-and-play Constrained Novel-View
Enhancement module to iteratively improve the quality of rendered novel views
during the Gaussian optimization process. Furthermore, we propose an Occlusion
Handling module, which flexibly removes occlusions utilizing the inherent
high-quality inpainting capability of constrained diffusion priors. Both
modules are capable of extracting appearance features from any user-provided
reference image, enabling flexible modeling of illumination-consistent scenes.
Extensive experiments on the PhotoTourism and Tanks and Temples datasets
demonstrate that SparseGS-W achieves state-of-the-art performance not only in
full-reference metrics, but also in commonly used non-reference metrics such as
FID, ClipIQA, and MUSIQ.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:40:40 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Li",
"Yiqing",
""
],
[
"Wang",
"Xuan",
""
],
[
"Wu",
"Jiawei",
""
],
[
"Ma",
"Yikun",
""
],
[
"Jin",
"Zhi",
""
]
] | TITLE: SparseGS-W: Sparse-View 3D Gaussian Splatting in the Wild with
Generative Priors
ABSTRACT: Synthesizing novel views of large-scale scenes from unconstrained in-the-wild
images is an important but challenging task in computer vision. Existing
methods, which optimize per-image appearance and transient occlusion through
implicit neural networks from dense training views (approximately 1000 images),
struggle to perform effectively under sparse input conditions, resulting in
noticeable artifacts. To this end, we propose SparseGS-W, a novel framework
based on 3D Gaussian Splatting that enables the reconstruction of complex
outdoor scenes and handles occlusions and appearance changes with as few as
five training images. We leverage geometric priors and constrained diffusion
priors to compensate for the lack of multi-view information from extremely
sparse input. Specifically, we propose a plug-and-play Constrained Novel-View
Enhancement module to iteratively improve the quality of rendered novel views
during the Gaussian optimization process. Furthermore, we propose an Occlusion
Handling module, which flexibly removes occlusions utilizing the inherent
high-quality inpainting capability of constrained diffusion priors. Both
modules are capable of extracting appearance features from any user-provided
reference image, enabling flexible modeling of illumination-consistent scenes.
Extensive experiments on the PhotoTourism and Tanks and Temples datasets
demonstrate that SparseGS-W achieves state-of-the-art performance not only in
full-reference metrics, but also in commonly used non-reference metrics such as
FID, ClipIQA, and MUSIQ.
| no_new_dataset | 0.946646 |
2503.19455 | Bo Yan | Bo Yan, Zhongjian Zhang, Huabin Sun, Mengmei Zhang, Yang Cao, Chuan
Shi | Data-centric Federated Graph Learning with Large Language Models | ongoing work | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In federated graph learning (FGL), a complete graph is divided into multiple
subgraphs stored in each client due to privacy concerns, and all clients
jointly train a global graph model by only transmitting model parameters. A
pain point of FGL is the heterogeneity problem, where nodes or structures
present non-IID properties among clients (e.g., different node label
distributions), dramatically undermining the convergence and performance of
FGL. To address this, existing efforts focus on design strategies at the model
level, i.e., they design models to extract common knowledge to mitigate
heterogeneity. However, these model-level strategies fail to fundamentally
address the heterogeneity problem as the model needs to be designed from
scratch when transferring to other tasks. Motivated by large language models
(LLMs) having achieved remarkable success, we aim to utilize LLMs to fully
understand and augment local text-attributed graphs, to address data
heterogeneity at the data level. In this paper, we propose a general framework
LLM4FGL that innovatively decomposes the task of LLM for FGL into two sub-tasks
theoretically. Specifically, for each client, it first utilizes the LLM to
generate missing neighbors and then infers connections between generated nodes
and raw nodes. To improve the quality of generated nodes, we design a novel
federated generation-and-reflection mechanism for LLMs, without the need to
modify the parameters of the LLM but relying solely on the collective feedback
from all clients. After neighbor generation, all the clients utilize a
pre-trained edge predictor to infer the missing edges. Furthermore, our
framework can seamlessly integrate as a plug-in with existing FGL methods.
Experiments on three real-world datasets demonstrate the superiority of our
method compared to advanced baselines.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:43:08 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Yan",
"Bo",
""
],
[
"Zhang",
"Zhongjian",
""
],
[
"Sun",
"Huabin",
""
],
[
"Zhang",
"Mengmei",
""
],
[
"Cao",
"Yang",
""
],
[
"Shi",
"Chuan",
""
]
] | TITLE: Data-centric Federated Graph Learning with Large Language Models
ABSTRACT: In federated graph learning (FGL), a complete graph is divided into multiple
subgraphs stored in each client due to privacy concerns, and all clients
jointly train a global graph model by only transmitting model parameters. A
pain point of FGL is the heterogeneity problem, where nodes or structures
present non-IID properties among clients (e.g., different node label
distributions), dramatically undermining the convergence and performance of
FGL. To address this, existing efforts focus on design strategies at the model
level, i.e., they design models to extract common knowledge to mitigate
heterogeneity. However, these model-level strategies fail to fundamentally
address the heterogeneity problem as the model needs to be designed from
scratch when transferring to other tasks. Motivated by large language models
(LLMs) having achieved remarkable success, we aim to utilize LLMs to fully
understand and augment local text-attributed graphs, to address data
heterogeneity at the data level. In this paper, we propose a general framework
LLM4FGL that innovatively decomposes the task of LLM for FGL into two sub-tasks
theoretically. Specifically, for each client, it first utilizes the LLM to
generate missing neighbors and then infers connections between generated nodes
and raw nodes. To improve the quality of generated nodes, we design a novel
federated generation-and-reflection mechanism for LLMs, without the need to
modify the parameters of the LLM but relying solely on the collective feedback
from all clients. After neighbor generation, all the clients utilize a
pre-trained edge predictor to infer the missing edges. Furthermore, our
framework can seamlessly integrate as a plug-in with existing FGL methods.
Experiments on three real-world datasets demonstrate the superiority of our
method compared to advanced baselines.
| no_new_dataset | 0.943815 |
2503.19462 | Haiyu Zhang | Haiyu Zhang and Xinyuan Chen and Yaohui Wang and Xihui Liu and Yunhong
Wang and Yu Qiao | AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset | Project Page: https://aejion.github.io/accvideo/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Diffusion models have achieved remarkable progress in the field of video
generation. However, their iterative denoising nature requires a large number
of inference steps to generate a video, which is slow and computationally
expensive. In this paper, we begin with a detailed analysis of the challenges
present in existing diffusion distillation methods and propose a novel
efficient method, namely AccVideo, to reduce the inference steps for
accelerating video diffusion models with synthetic dataset. We leverage the
pretrained video diffusion model to generate multiple valid denoising
trajectories as our synthetic dataset, which eliminates the use of useless data
points during distillation. Based on the synthetic dataset, we design a
trajectory-based few-step guidance that utilizes key data points from the
denoising trajectories to learn the noise-to-video mapping, enabling video
generation in fewer steps. Furthermore, since the synthetic dataset captures
the data distribution at each diffusion timestep, we introduce an adversarial
training strategy to align the output distribution of the student model with
that of our synthetic dataset, thereby enhancing the video quality. Extensive
experiments demonstrate that our model achieves 8.5x improvements in generation
speed compared to the teacher model while maintaining comparable performance.
Compared to previous accelerating methods, our approach is capable of
generating videos with higher quality and resolution, i.e., 5-seconds,
720x1280, 24fps.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 08:52:07 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhang",
"Haiyu",
""
],
[
"Chen",
"Xinyuan",
""
],
[
"Wang",
"Yaohui",
""
],
[
"Liu",
"Xihui",
""
],
[
"Wang",
"Yunhong",
""
],
[
"Qiao",
"Yu",
""
]
] | TITLE: AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
ABSTRACT: Diffusion models have achieved remarkable progress in the field of video
generation. However, their iterative denoising nature requires a large number
of inference steps to generate a video, which is slow and computationally
expensive. In this paper, we begin with a detailed analysis of the challenges
present in existing diffusion distillation methods and propose a novel
efficient method, namely AccVideo, to reduce the inference steps for
accelerating video diffusion models with synthetic dataset. We leverage the
pretrained video diffusion model to generate multiple valid denoising
trajectories as our synthetic dataset, which eliminates the use of useless data
points during distillation. Based on the synthetic dataset, we design a
trajectory-based few-step guidance that utilizes key data points from the
denoising trajectories to learn the noise-to-video mapping, enabling video
generation in fewer steps. Furthermore, since the synthetic dataset captures
the data distribution at each diffusion timestep, we introduce an adversarial
training strategy to align the output distribution of the student model with
that of our synthetic dataset, thereby enhancing the video quality. Extensive
experiments demonstrate that our model achieves 8.5x improvements in generation
speed compared to the teacher model while maintaining comparable performance.
Compared to previous accelerating methods, our approach is capable of
generating videos with higher quality and resolution, i.e., 5-seconds,
720x1280, 24fps.
| no_new_dataset | 0.897201 |
2503.19476 | Chuqin Geng | Chuqin Geng, Zhaoyue Wang, Ziyu Zhao, Haolin Ye, Xujie Si | Extracting Interpretable Logic Rules from Graph Neural Networks | 12 pages, 4 figures | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Graph neural networks (GNNs) operate over both input feature spaces and
combinatorial graph structures, making it challenging to understand the
rationale behind their predictions. As GNNs gain widespread popularity and
demonstrate success across various domains, such as drug discovery, studying
their interpretability has become a critical task. To address this, many
explainability methods have been proposed, with recent efforts shifting from
instance-specific explanations to global concept-based explainability. However,
these approaches face several limitations, such as relying on predefined
concepts and explaining only a limited set of patterns. To address this, we
propose a novel framework, LOGICXGNN, for extracting interpretable logic rules
from GNNs. LOGICXGNN is model-agnostic, efficient, and data-driven, eliminating
the need for predefined concepts. More importantly, it can serve as a
rule-based classifier and even outperform the original neural models. Its
interpretability facilitates knowledge discovery, as demonstrated by its
ability to extract detailed and accurate chemistry knowledge that is often
overlooked by existing methods. Another key advantage of LOGICXGNN is its
ability to generate new graph instances in a controlled and transparent manner,
offering significant potential for applications such as drug design. We
empirically demonstrate these merits through experiments on real-world datasets
such as MUTAG and BBBP.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 09:09:46 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Geng",
"Chuqin",
""
],
[
"Wang",
"Zhaoyue",
""
],
[
"Zhao",
"Ziyu",
""
],
[
"Ye",
"Haolin",
""
],
[
"Si",
"Xujie",
""
]
] | TITLE: Extracting Interpretable Logic Rules from Graph Neural Networks
ABSTRACT: Graph neural networks (GNNs) operate over both input feature spaces and
combinatorial graph structures, making it challenging to understand the
rationale behind their predictions. As GNNs gain widespread popularity and
demonstrate success across various domains, such as drug discovery, studying
their interpretability has become a critical task. To address this, many
explainability methods have been proposed, with recent efforts shifting from
instance-specific explanations to global concept-based explainability. However,
these approaches face several limitations, such as relying on predefined
concepts and explaining only a limited set of patterns. To address this, we
propose a novel framework, LOGICXGNN, for extracting interpretable logic rules
from GNNs. LOGICXGNN is model-agnostic, efficient, and data-driven, eliminating
the need for predefined concepts. More importantly, it can serve as a
rule-based classifier and even outperform the original neural models. Its
interpretability facilitates knowledge discovery, as demonstrated by its
ability to extract detailed and accurate chemistry knowledge that is often
overlooked by existing methods. Another key advantage of LOGICXGNN is its
ability to generate new graph instances in a controlled and transparent manner,
offering significant potential for applications such as drug design. We
empirically demonstrate these merits through experiments on real-world datasets
such as MUTAG and BBBP.
| no_new_dataset | 0.942771 |
2503.19486 | Zhengwentai Sun | Zhengwentai Sun, Heyuan Li, Xihe Yang, Keru Zheng, Shuliang Ning,
Yihao Zhi, Hongjie Liao, Chenghong Li, Shuguang Cui, Xiaoguang Han | Exploring Disentangled and Controllable Human Image Synthesis: From
End-to-End to Stage-by-Stage | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Achieving fine-grained controllability in human image synthesis is a
long-standing challenge in computer vision. Existing methods primarily focus on
either facial synthesis or near-frontal body generation, with limited ability
to simultaneously control key factors such as viewpoint, pose, clothing, and
identity in a disentangled manner. In this paper, we introduce a new
disentangled and controllable human synthesis task, which explicitly separates
and manipulates these four factors within a unified framework. We first develop
an end-to-end generative model trained on MVHumanNet for factor
disentanglement. However, the domain gap between MVHumanNet and in-the-wild
data produce unsatisfacotry results, motivating the exploration of virtual
try-on (VTON) dataset as a potential solution. Through experiments, we observe
that simply incorporating the VTON dataset as additional data to train the
end-to-end model degrades performance, primarily due to the inconsistency in
data forms between the two datasets, which disrupts the disentanglement
process. To better leverage both datasets, we propose a stage-by-stage
framework that decomposes human image generation into three sequential steps:
clothed A-pose generation, back-view synthesis, and pose and view control. This
structured pipeline enables better dataset utilization at different stages,
significantly improving controllability and generalization, especially for
in-the-wild scenarios. Extensive experiments demonstrate that our
stage-by-stage approach outperforms end-to-end models in both visual fidelity
and disentanglement quality, offering a scalable solution for real-world tasks.
Additional demos are available on the project page:
https://taited.github.io/discohuman-project/.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 09:23:20 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Sun",
"Zhengwentai",
""
],
[
"Li",
"Heyuan",
""
],
[
"Yang",
"Xihe",
""
],
[
"Zheng",
"Keru",
""
],
[
"Ning",
"Shuliang",
""
],
[
"Zhi",
"Yihao",
""
],
[
"Liao",
"Hongjie",
""
],
[
"Li",
"Chenghong",
""
],
[
"Cui",
"Shuguang",
""
],
[
"Han",
"Xiaoguang",
""
]
] | TITLE: Exploring Disentangled and Controllable Human Image Synthesis: From
End-to-End to Stage-by-Stage
ABSTRACT: Achieving fine-grained controllability in human image synthesis is a
long-standing challenge in computer vision. Existing methods primarily focus on
either facial synthesis or near-frontal body generation, with limited ability
to simultaneously control key factors such as viewpoint, pose, clothing, and
identity in a disentangled manner. In this paper, we introduce a new
disentangled and controllable human synthesis task, which explicitly separates
and manipulates these four factors within a unified framework. We first develop
an end-to-end generative model trained on MVHumanNet for factor
disentanglement. However, the domain gap between MVHumanNet and in-the-wild
data produce unsatisfacotry results, motivating the exploration of virtual
try-on (VTON) dataset as a potential solution. Through experiments, we observe
that simply incorporating the VTON dataset as additional data to train the
end-to-end model degrades performance, primarily due to the inconsistency in
data forms between the two datasets, which disrupts the disentanglement
process. To better leverage both datasets, we propose a stage-by-stage
framework that decomposes human image generation into three sequential steps:
clothed A-pose generation, back-view synthesis, and pose and view control. This
structured pipeline enables better dataset utilization at different stages,
significantly improving controllability and generalization, especially for
in-the-wild scenarios. Extensive experiments demonstrate that our
stage-by-stage approach outperforms end-to-end models in both visual fidelity
and disentanglement quality, offering a scalable solution for real-world tasks.
Additional demos are available on the project page:
https://taited.github.io/discohuman-project/.
| no_new_dataset | 0.950686 |
2503.19506 | Yongxin Ma | Yongxin Ma, Jie Xu, Shenghai Yuan, Tian Zhi, Wenlu Yu, Jun Zhou, and
Lihua Xie | MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate
Environments | Accepted by IEEE Transactions on Intelligent Vehicles | null | 10.1109/TIV.2024.3414852 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SLAM plays a crucial role in automation tasks, such as warehouse logistics,
healthcare robotics, and restaurant delivery. These scenes come with various
challenges, including navigating around crowds of people, dealing with flying
plastic bags that can temporarily blind sensors, and addressing reduced LiDAR
density caused by cooking smoke. Such scenarios can result in over-degeneracy,
causing the map to drift. To address this issue, this paper presents a
multi-map LiDAR-inertial system (MM-LINS) for the first time. The front-end
employs an iterated error state Kalman filter for state estimation and
introduces a reliable evaluation strategy for degeneracy detection. If
over-degeneracy is detected, the active map will be stored into sleeping maps.
Subsequently, the system continuously attempts to construct new maps using a
dynamic initialization method to ensure successful initialization upon leaving
the over-degeneracy. Regarding the back-end, the Scan Context descriptor is
utilized to detect inter-map similarity. Upon successful recognition of a
sleeping map that shares a common region with the active map, the overlapping
trajectory region is utilized to constrain the positional transformation near
the edge of the prior map. In response to this, a constraint-enhanced map
fusion strategy is proposed to achieve high-precision positional and mapping
results. Experiments have been conducted separately on both public datasets
that exhibited over-degenerate conditions and in real-world environments. These
tests demonstrated the effectiveness of MM-LINS in over-degeneracy environment.
Our codes are open-sourced on Github.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 09:57:21 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Ma",
"Yongxin",
""
],
[
"Xu",
"Jie",
""
],
[
"Yuan",
"Shenghai",
""
],
[
"Zhi",
"Tian",
""
],
[
"Yu",
"Wenlu",
""
],
[
"Zhou",
"Jun",
""
],
[
"Xie",
"Lihua",
""
]
] | TITLE: MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate
Environments
ABSTRACT: SLAM plays a crucial role in automation tasks, such as warehouse logistics,
healthcare robotics, and restaurant delivery. These scenes come with various
challenges, including navigating around crowds of people, dealing with flying
plastic bags that can temporarily blind sensors, and addressing reduced LiDAR
density caused by cooking smoke. Such scenarios can result in over-degeneracy,
causing the map to drift. To address this issue, this paper presents a
multi-map LiDAR-inertial system (MM-LINS) for the first time. The front-end
employs an iterated error state Kalman filter for state estimation and
introduces a reliable evaluation strategy for degeneracy detection. If
over-degeneracy is detected, the active map will be stored into sleeping maps.
Subsequently, the system continuously attempts to construct new maps using a
dynamic initialization method to ensure successful initialization upon leaving
the over-degeneracy. Regarding the back-end, the Scan Context descriptor is
utilized to detect inter-map similarity. Upon successful recognition of a
sleeping map that shares a common region with the active map, the overlapping
trajectory region is utilized to constrain the positional transformation near
the edge of the prior map. In response to this, a constraint-enhanced map
fusion strategy is proposed to achieve high-precision positional and mapping
results. Experiments have been conducted separately on both public datasets
that exhibited over-degenerate conditions and in real-world environments. These
tests demonstrated the effectiveness of MM-LINS in over-degeneracy environment.
Our codes are open-sourced on Github.
| no_new_dataset | 0.948728 |
2503.19508 | Kartik Jangra | Kartik Jangra, Aman Kumar Singh, Yashwani Mann, Geetanjali Rathee | Improved Alignment of Modalities in Large Vision Language Models | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent advancements in vision-language models have achieved remarkable
results in making language models understand vision inputs. However, a unified
approach to align these models across diverse tasks such as image captioning
and visual question answering remains a challenge. Existing methods either
require very big language models or very big datasets which is not efficient in
utilizing existing models. This paper addresses this gap and devises a training
strategy of auto-regressive vision-language models, to unify vision-language
tasks like image-captioning and visual question answering. We propose four
training stages for aligning the vision model with the language model, in other
words, the language model is given an ability to process visual inputs. We also
devise different attention masks for training transformer-based language models
that improve the quality of visual features. Further, we introduce some
findings, 1) the attention mask should not be applied on visual inputs, 2) the
Language model converges faster on AI- generated data, 3) More work should be
done in the alignment stage during the pre-training of the model, 4) the model
can easily adapt to any downstream tasks like visual question answering on
healthcare datasets like PathVQA. After training the model for one epoch for
all the stages, it outperforms large models like VILA-13 billion models on
common benchmarks like CIDEr scores on COCO and Flickr30k datasets and achieves
very close scores to GIT-2 on the same dataset despite being a much smaller
model trained on a much smaller dataset. All of the training is done using best
practices available like multi- GPU parallel training, lower-precision training
with 16-bit float numbers, faster attention (SDPA), and gradient accumulation,
and completed the training within 12 hours.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 09:59:46 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Jangra",
"Kartik",
""
],
[
"Singh",
"Aman Kumar",
""
],
[
"Mann",
"Yashwani",
""
],
[
"Rathee",
"Geetanjali",
""
]
] | TITLE: Improved Alignment of Modalities in Large Vision Language Models
ABSTRACT: Recent advancements in vision-language models have achieved remarkable
results in making language models understand vision inputs. However, a unified
approach to align these models across diverse tasks such as image captioning
and visual question answering remains a challenge. Existing methods either
require very big language models or very big datasets which is not efficient in
utilizing existing models. This paper addresses this gap and devises a training
strategy of auto-regressive vision-language models, to unify vision-language
tasks like image-captioning and visual question answering. We propose four
training stages for aligning the vision model with the language model, in other
words, the language model is given an ability to process visual inputs. We also
devise different attention masks for training transformer-based language models
that improve the quality of visual features. Further, we introduce some
findings, 1) the attention mask should not be applied on visual inputs, 2) the
Language model converges faster on AI- generated data, 3) More work should be
done in the alignment stage during the pre-training of the model, 4) the model
can easily adapt to any downstream tasks like visual question answering on
healthcare datasets like PathVQA. After training the model for one epoch for
all the stages, it outperforms large models like VILA-13 billion models on
common benchmarks like CIDEr scores on COCO and Flickr30k datasets and achieves
very close scores to GIT-2 on the same dataset despite being a much smaller
model trained on a much smaller dataset. All of the training is done using best
practices available like multi- GPU parallel training, lower-precision training
with 16-bit float numbers, faster attention (SDPA), and gradient accumulation,
and completed the training within 12 hours.
| no_new_dataset | 0.952309 |
2503.19525 | Edoardo Bianchi | Edoardo Bianchi | Beyond Relevance: An Adaptive Exploration-Based Framework for
Personalized Recommendations | null | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Recommender systems must balance personalization, diversity, and robustness
to cold-start scenarios to remain effective in dynamic content environments.
This paper introduces an adaptive, exploration-based recommendation framework
that adjusts to evolving user preferences and content distributions to promote
diversity and novelty without compromising relevance. The system represents
items using sentence-transformer embeddings and organizes them into
semantically coherent clusters through an online algorithm with adaptive
thresholding. A user-controlled exploration mechanism enhances diversity by
selectively sampling from under-explored clusters. Experiments on the MovieLens
dataset show that enabling exploration reduces intra-list similarity from 0.34
to 0.26 and increases unexpectedness to 0.73, outperforming collaborative
filtering and popularity-based baselines. A/B testing with 300 simulated users
reveals a strong link between interaction history and preference for diversity,
with 72.7% of long-term users favoring exploratory recommendations.
Computational analysis confirms that clustering and recommendation processes
scale linearly with the number of clusters. These results demonstrate that
adaptive exploration effectively mitigates over-specialization while preserving
personalization and efficiency.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 10:27:32 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Bianchi",
"Edoardo",
""
]
] | TITLE: Beyond Relevance: An Adaptive Exploration-Based Framework for
Personalized Recommendations
ABSTRACT: Recommender systems must balance personalization, diversity, and robustness
to cold-start scenarios to remain effective in dynamic content environments.
This paper introduces an adaptive, exploration-based recommendation framework
that adjusts to evolving user preferences and content distributions to promote
diversity and novelty without compromising relevance. The system represents
items using sentence-transformer embeddings and organizes them into
semantically coherent clusters through an online algorithm with adaptive
thresholding. A user-controlled exploration mechanism enhances diversity by
selectively sampling from under-explored clusters. Experiments on the MovieLens
dataset show that enabling exploration reduces intra-list similarity from 0.34
to 0.26 and increases unexpectedness to 0.73, outperforming collaborative
filtering and popularity-based baselines. A/B testing with 300 simulated users
reveals a strong link between interaction history and preference for diversity,
with 72.7% of long-term users favoring exploratory recommendations.
Computational analysis confirms that clustering and recommendation processes
scale linearly with the number of clusters. These results demonstrate that
adaptive exploration effectively mitigates over-specialization while preserving
personalization and efficiency.
| no_new_dataset | 0.942665 |
2503.19530 | Suhas Hegde | Suhas G Hegde, Shilpy Kaur, Aruna Tiwari | VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained
Foundation Models | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Popular PEFT methods achieve parameter efficiency by assuming that
incremental weight updates are inherently low-rank, which often leads to a
performance gap compared to full fine-tuning. While recent methods have
attempted to address this limitation, they typically lack sufficient parameter
and memory efficiency. We propose VectorFit, an effective and easily deployable
approach that adaptively trains the singular vectors and biases of pre-trained
weight matrices. We demonstrate that the utilization of structural and
transformational characteristics of pre-trained weights enables high-rank
updates comparable to those of full fine-tuning. As a result, VectorFit
achieves superior performance with 9X less trainable parameters compared to
state-of-the-art PEFT methods. Through extensive experiments over 17 datasets
spanning diverse language and vision tasks such as natural language
understanding and generation, question answering, image classification, and
image generation, we exhibit that VectorFit consistently outperforms baselines,
even in extremely low-budget scenarios.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 10:36:27 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Hegde",
"Suhas G",
""
],
[
"Kaur",
"Shilpy",
""
],
[
"Tiwari",
"Aruna",
""
]
] | TITLE: VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained
Foundation Models
ABSTRACT: Popular PEFT methods achieve parameter efficiency by assuming that
incremental weight updates are inherently low-rank, which often leads to a
performance gap compared to full fine-tuning. While recent methods have
attempted to address this limitation, they typically lack sufficient parameter
and memory efficiency. We propose VectorFit, an effective and easily deployable
approach that adaptively trains the singular vectors and biases of pre-trained
weight matrices. We demonstrate that the utilization of structural and
transformational characteristics of pre-trained weights enables high-rank
updates comparable to those of full fine-tuning. As a result, VectorFit
achieves superior performance with 9X less trainable parameters compared to
state-of-the-art PEFT methods. Through extensive experiments over 17 datasets
spanning diverse language and vision tasks such as natural language
understanding and generation, question answering, image classification, and
image generation, we exhibit that VectorFit consistently outperforms baselines,
even in extremely low-budget scenarios.
| no_new_dataset | 0.945197 |
2503.19543 | Jiaming Zhang | Junwei Zheng, Ruiping Liu, Yufan Chen, Zhenfang Chen, Kailun Yang,
Jiaming Zhang, Rainer Stiefelhagen | Scene-agnostic Pose Regression for Visual Localization | Accepted by CVPR 2025. Project page:
https://junweizheng93.github.io/publications/SPR/SPR.html | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Absolute Pose Regression (APR) predicts 6D camera poses but lacks the
adaptability to unknown environments without retraining, while Relative Pose
Regression (RPR) generalizes better yet requires a large image retrieval
database. Visual Odometry (VO) generalizes well in unseen environments but
suffers from accumulated error in open trajectories. To address this dilemma,
we introduce a new task, Scene-agnostic Pose Regression (SPR), which can
achieve accurate pose regression in a flexible way while eliminating the need
for retraining or databases. To benchmark SPR, we created a large-scale
dataset, 360SPR, with over 200K photorealistic panoramas, 3.6M pinhole images
and camera poses in 270 scenes at three different sensor heights. Furthermore,
a SPR-Mamba model is initially proposed to address SPR in a dual-branch manner.
Extensive experiments and studies demonstrate the effectiveness of our SPR
paradigm, dataset, and model. In the unknown scenes of both 360SPR and 360Loc
datasets, our method consistently outperforms APR, RPR and VO. The dataset and
code are available at
https://junweizheng93.github.io/publications/SPR/SPR.html.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 10:58:40 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zheng",
"Junwei",
""
],
[
"Liu",
"Ruiping",
""
],
[
"Chen",
"Yufan",
""
],
[
"Chen",
"Zhenfang",
""
],
[
"Yang",
"Kailun",
""
],
[
"Zhang",
"Jiaming",
""
],
[
"Stiefelhagen",
"Rainer",
""
]
] | TITLE: Scene-agnostic Pose Regression for Visual Localization
ABSTRACT: Absolute Pose Regression (APR) predicts 6D camera poses but lacks the
adaptability to unknown environments without retraining, while Relative Pose
Regression (RPR) generalizes better yet requires a large image retrieval
database. Visual Odometry (VO) generalizes well in unseen environments but
suffers from accumulated error in open trajectories. To address this dilemma,
we introduce a new task, Scene-agnostic Pose Regression (SPR), which can
achieve accurate pose regression in a flexible way while eliminating the need
for retraining or databases. To benchmark SPR, we created a large-scale
dataset, 360SPR, with over 200K photorealistic panoramas, 3.6M pinhole images
and camera poses in 270 scenes at three different sensor heights. Furthermore,
a SPR-Mamba model is initially proposed to address SPR in a dual-branch manner.
Extensive experiments and studies demonstrate the effectiveness of our SPR
paradigm, dataset, and model. In the unknown scenes of both 360SPR and 360Loc
datasets, our method consistently outperforms APR, RPR and VO. The dataset and
code are available at
https://junweizheng93.github.io/publications/SPR/SPR.html.
| new_dataset | 0.957952 |
2503.19545 | Elena Buglakova | Elena Buglakova, Anwai Archit, Edoardo D'Imprima, Julia Mahamid,
Constantin Pape, Anna Kreshuk | Tiling artifacts and trade-offs of feature normalization in the
segmentation of large biological images | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Segmentation of very large images is a common problem in microscopy, medical
imaging or remote sensing. The problem is usually addressed by sliding window
inference, which can theoretically lead to seamlessly stitched predictions.
However, in practice many of the popular pipelines still suffer from tiling
artifacts. We investigate the root cause of these issues and show that they
stem from the normalization layers within the neural networks. We propose
indicators to detect normalization issues and further explore the trade-offs
between artifact-free and high-quality predictions, using three diverse
microscopy datasets as examples. Finally, we propose to use BatchRenorm as the
most suitable normalization strategy, which effectively removes tiling
artifacts and enhances transfer performance, thereby improving the reusability
of trained networks for new datasets.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 11:00:37 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Buglakova",
"Elena",
""
],
[
"Archit",
"Anwai",
""
],
[
"D'Imprima",
"Edoardo",
""
],
[
"Mahamid",
"Julia",
""
],
[
"Pape",
"Constantin",
""
],
[
"Kreshuk",
"Anna",
""
]
] | TITLE: Tiling artifacts and trade-offs of feature normalization in the
segmentation of large biological images
ABSTRACT: Segmentation of very large images is a common problem in microscopy, medical
imaging or remote sensing. The problem is usually addressed by sliding window
inference, which can theoretically lead to seamlessly stitched predictions.
However, in practice many of the popular pipelines still suffer from tiling
artifacts. We investigate the root cause of these issues and show that they
stem from the normalization layers within the neural networks. We propose
indicators to detect normalization issues and further explore the trade-offs
between artifact-free and high-quality predictions, using three diverse
microscopy datasets as examples. Finally, we propose to use BatchRenorm as the
most suitable normalization strategy, which effectively removes tiling
artifacts and enhances transfer performance, thereby improving the reusability
of trained networks for new datasets.
| no_new_dataset | 0.952131 |
2503.19549 | Zubair Shaban PhD | Zubair Shaban, Nazreen Shah, Ranjitha Prasad | Noise Resilient Over-The-Air Federated Learning In Heterogeneous
Wireless Networks | null | null | null | null | cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | In 6G wireless networks, Artificial Intelligence (AI)-driven applications
demand the adoption of Federated Learning (FL) to enable efficient and
privacy-preserving model training across distributed devices. Over-The-Air
Federated Learning (OTA-FL) exploits the superposition property of multiple
access channels, allowing edge users in 6G networks to efficiently share
spectral resources and perform low-latency global model aggregation. However,
these advantages come with challenges, as traditional OTA-FL techniques suffer
due to the joint effects of Additive White Gaussian Noise (AWGN) at the server,
fading, and both data and system heterogeneity at the participating edge
devices. In this work, we propose the novel Noise Resilient Over-the-Air
Federated Learning (NoROTA-FL) framework to jointly tackle these challenges in
federated wireless networks. In NoROTA-FL, the local optimization problems find
controlled inexact solutions, which manifests as an additional proximal
constraint at the clients. This approach provides robustness against
straggler-induced partial work, heterogeneity, noise, and fading. From a
theoretical perspective, we leverage the zeroth- and first-order inexactness
and establish convergence guarantees for non-convex optimization problems in
the presence of heterogeneous data and varying system capabilities.
Experimentally, we validate NoROTA-FL on real-world datasets, including
FEMNIST, CIFAR10, and CIFAR100, demonstrating its robustness in noisy and
heterogeneous environments. Compared to state-of-the-art baselines such as
COTAF and FedProx, NoROTA-FL achieves significantly more stable convergence and
higher accuracy, particularly in the presence of stragglers.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 11:04:00 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Shaban",
"Zubair",
""
],
[
"Shah",
"Nazreen",
""
],
[
"Prasad",
"Ranjitha",
""
]
] | TITLE: Noise Resilient Over-The-Air Federated Learning In Heterogeneous
Wireless Networks
ABSTRACT: In 6G wireless networks, Artificial Intelligence (AI)-driven applications
demand the adoption of Federated Learning (FL) to enable efficient and
privacy-preserving model training across distributed devices. Over-The-Air
Federated Learning (OTA-FL) exploits the superposition property of multiple
access channels, allowing edge users in 6G networks to efficiently share
spectral resources and perform low-latency global model aggregation. However,
these advantages come with challenges, as traditional OTA-FL techniques suffer
due to the joint effects of Additive White Gaussian Noise (AWGN) at the server,
fading, and both data and system heterogeneity at the participating edge
devices. In this work, we propose the novel Noise Resilient Over-the-Air
Federated Learning (NoROTA-FL) framework to jointly tackle these challenges in
federated wireless networks. In NoROTA-FL, the local optimization problems find
controlled inexact solutions, which manifests as an additional proximal
constraint at the clients. This approach provides robustness against
straggler-induced partial work, heterogeneity, noise, and fading. From a
theoretical perspective, we leverage the zeroth- and first-order inexactness
and establish convergence guarantees for non-convex optimization problems in
the presence of heterogeneous data and varying system capabilities.
Experimentally, we validate NoROTA-FL on real-world datasets, including
FEMNIST, CIFAR10, and CIFAR100, demonstrating its robustness in noisy and
heterogeneous environments. Compared to state-of-the-art baselines such as
COTAF and FedProx, NoROTA-FL achieves significantly more stable convergence and
higher accuracy, particularly in the presence of stragglers.
| no_new_dataset | 0.950549 |
2503.19592 | Xinxing Cheng | Xinxing Cheng, Tianyang Zhang, Wenqi Lu, Qingjie Meng, Alejandro F.
Frangi, Jinming Duan | SACB-Net: Spatial-awareness Convolutions for Medical Image Registration | CVPR 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning-based image registration methods have shown state-of-the-art
performance and rapid inference speeds. Despite these advances, many existing
approaches fall short in capturing spatially varying information in non-local
regions of feature maps due to the reliance on spatially-shared convolution
kernels. This limitation leads to suboptimal estimation of deformation fields.
In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to
enhance the spatial information within feature representations. Our SACB
estimates the spatial clusters within feature maps by leveraging feature
similarity and subsequently parameterizes the adaptive convolution kernels
across diverse regions. This adaptive mechanism generates the convolution
kernels (weights and biases) tailored to spatial variations, thereby enabling
the network to effectively capture spatially varying information. Building on
SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates
SACBs to facilitate multi-scale flow composition, particularly addressing large
deformations. Experimental results on the brain IXI and LPBA datasets as well
as Abdomen CT datasets demonstrate the effectiveness of SACB and the
superiority of SACB-Net over the state-of-the-art learning-based registration
methods. The code is available at https://github.com/x-xc/SACB_Net .
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 12:14:21 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Cheng",
"Xinxing",
""
],
[
"Zhang",
"Tianyang",
""
],
[
"Lu",
"Wenqi",
""
],
[
"Meng",
"Qingjie",
""
],
[
"Frangi",
"Alejandro F.",
""
],
[
"Duan",
"Jinming",
""
]
] | TITLE: SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
ABSTRACT: Deep learning-based image registration methods have shown state-of-the-art
performance and rapid inference speeds. Despite these advances, many existing
approaches fall short in capturing spatially varying information in non-local
regions of feature maps due to the reliance on spatially-shared convolution
kernels. This limitation leads to suboptimal estimation of deformation fields.
In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to
enhance the spatial information within feature representations. Our SACB
estimates the spatial clusters within feature maps by leveraging feature
similarity and subsequently parameterizes the adaptive convolution kernels
across diverse regions. This adaptive mechanism generates the convolution
kernels (weights and biases) tailored to spatial variations, thereby enabling
the network to effectively capture spatially varying information. Building on
SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates
SACBs to facilitate multi-scale flow composition, particularly addressing large
deformations. Experimental results on the brain IXI and LPBA datasets as well
as Abdomen CT datasets demonstrate the effectiveness of SACB and the
superiority of SACB-Net over the state-of-the-art learning-based registration
methods. The code is available at https://github.com/x-xc/SACB_Net .
| no_new_dataset | 0.946051 |
2503.19595 | Yunhao Tang | Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, R\'emi Munos | Optimizing Language Models for Inference Time Objectives using
Reinforcement Learning | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we investigate the merits of explicitly optimizing for
inference time algorithmic performance during model training. We show how
optimizing for inference time performance can improve overall model efficacy.
We consider generic inference time objectives with $k$ samples, with a focus on
pass@$k$ and majority voting as two main applications. With language model
training on reasoning datasets, we showcase the performance trade-off enabled
by training with such objectives. When training on code generation tasks, we
show that the approach significantly improves pass@$k$ objectives compared to
the baseline method.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 12:21:26 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Tang",
"Yunhao",
""
],
[
"Zheng",
"Kunhao",
""
],
[
"Synnaeve",
"Gabriel",
""
],
[
"Munos",
"Rémi",
""
]
] | TITLE: Optimizing Language Models for Inference Time Objectives using
Reinforcement Learning
ABSTRACT: In this work, we investigate the merits of explicitly optimizing for
inference time algorithmic performance during model training. We show how
optimizing for inference time performance can improve overall model efficacy.
We consider generic inference time objectives with $k$ samples, with a focus on
pass@$k$ and majority voting as two main applications. With language model
training on reasoning datasets, we showcase the performance trade-off enabled
by training with such objectives. When training on code generation tasks, we
show that the approach significantly improves pass@$k$ objectives compared to
the baseline method.
| no_new_dataset | 0.948202 |
2503.19599 | Sergey Mechtaev | Dimitrios Stamatios Bouras, Yihan Dai, Tairan Wang, Yingfei Xiong,
Sergey Mechtaev | HoarePrompt: Structural Reasoning About Program Correctness in Natural
Language | null | null | null | null | cs.SE cs.AI | http://creativecommons.org/licenses/by/4.0/ | While software requirements are often expressed in natural language,
verifying the correctness of a program against natural language requirements is
a hard and underexplored problem. Large language models (LLMs) are promising
candidates for addressing this challenge, however our experience shows that
they are ineffective in this task, often failing to detect even straightforward
bugs. To address this gap, we introduce HoarePrompt, a novel approach that
adapts fundamental ideas from program analysis and verification to natural
language artifacts. Drawing inspiration from the strongest postcondition
calculus, HoarePrompt employs a systematic, step-by-step process in which an
LLM generates natural language descriptions of reachable program states at
various points in the code. To manage loops, we propose few-shot-driven
k-induction, an adaptation of the k-induction method widely used in model
checking. Once program states are described, HoarePrompt leverages the LLM to
assess whether the program, annotated with these state descriptions, conforms
to the natural language requirements. For evaluating the quality of classifiers
of program correctness with respect to natural language requirements, we
constructed CoCoClaNeL, a challenging dataset of solutions to programming
competition problems. Our experiments show that HoarePrompt improves the MCC by
62% compared to directly using Zero-shot-CoT prompts for correctness
classification. Furthermore, HoarePrompt outperforms a classifier that assesses
correctness via LLM-based test generation by increasing the MCC by 93%. The
inductive reasoning mechanism contributes a 28% boost to MCC, underscoring its
effectiveness in managing loops.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 12:30:30 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Bouras",
"Dimitrios Stamatios",
""
],
[
"Dai",
"Yihan",
""
],
[
"Wang",
"Tairan",
""
],
[
"Xiong",
"Yingfei",
""
],
[
"Mechtaev",
"Sergey",
""
]
] | TITLE: HoarePrompt: Structural Reasoning About Program Correctness in Natural
Language
ABSTRACT: While software requirements are often expressed in natural language,
verifying the correctness of a program against natural language requirements is
a hard and underexplored problem. Large language models (LLMs) are promising
candidates for addressing this challenge, however our experience shows that
they are ineffective in this task, often failing to detect even straightforward
bugs. To address this gap, we introduce HoarePrompt, a novel approach that
adapts fundamental ideas from program analysis and verification to natural
language artifacts. Drawing inspiration from the strongest postcondition
calculus, HoarePrompt employs a systematic, step-by-step process in which an
LLM generates natural language descriptions of reachable program states at
various points in the code. To manage loops, we propose few-shot-driven
k-induction, an adaptation of the k-induction method widely used in model
checking. Once program states are described, HoarePrompt leverages the LLM to
assess whether the program, annotated with these state descriptions, conforms
to the natural language requirements. For evaluating the quality of classifiers
of program correctness with respect to natural language requirements, we
constructed CoCoClaNeL, a challenging dataset of solutions to programming
competition problems. Our experiments show that HoarePrompt improves the MCC by
62% compared to directly using Zero-shot-CoT prompts for correctness
classification. Furthermore, HoarePrompt outperforms a classifier that assesses
correctness via LLM-based test generation by increasing the MCC by 93%. The
inductive reasoning mechanism contributes a 28% boost to MCC, underscoring its
effectiveness in managing loops.
| new_dataset | 0.95594 |
2503.19606 | Prince Gideon Kubendran Amos | Deepti Madurai Muthu, Priyanka S, Lalitha Rani N, and P. G. Kubendran
Amos | Single Shot AI-assisted quantification of KI-67 proliferation index in
breast cancer | null | null | null | null | eess.IV cs.CV q-bio.QM q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Reliable quantification of Ki-67, a key proliferation marker in breast
cancer, is essential for molecular subtyping and informed treatment planning.
Conventional approaches, including visual estimation and manual counting,
suffer from interobserver variability and limited reproducibility. This study
introduces an AI-assisted method using the YOLOv8 object detection framework
for automated Ki-67 scoring. High-resolution digital images (40x magnification)
of immunohistochemically stained tumor sections were captured from Ki-67
hotspot regions and manually annotated by a domain expert to distinguish
Ki-67-positive and negative tumor cells. The dataset was augmented and divided
into training (80%), validation (10%), and testing (10%) subsets. Among the
YOLOv8 variants tested, the Medium model achieved the highest performance, with
a mean Average Precision at 50% Intersection over Union (mAP50) exceeding 85%
for Ki-67-positive cells. The proposed approach offers an efficient, scalable,
and objective alternative to conventional scoring methods, supporting greater
consistency in Ki-67 evaluation. Future directions include developing
user-friendly clinical interfaces and expanding to multi-institutional datasets
to enhance generalizability and facilitate broader adoption in diagnostic
practice.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 12:41:45 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Muthu",
"Deepti Madurai",
""
],
[
"S",
"Priyanka",
""
],
[
"N",
"Lalitha Rani",
""
],
[
"Amos",
"P. G. Kubendran",
""
]
] | TITLE: Single Shot AI-assisted quantification of KI-67 proliferation index in
breast cancer
ABSTRACT: Reliable quantification of Ki-67, a key proliferation marker in breast
cancer, is essential for molecular subtyping and informed treatment planning.
Conventional approaches, including visual estimation and manual counting,
suffer from interobserver variability and limited reproducibility. This study
introduces an AI-assisted method using the YOLOv8 object detection framework
for automated Ki-67 scoring. High-resolution digital images (40x magnification)
of immunohistochemically stained tumor sections were captured from Ki-67
hotspot regions and manually annotated by a domain expert to distinguish
Ki-67-positive and negative tumor cells. The dataset was augmented and divided
into training (80%), validation (10%), and testing (10%) subsets. Among the
YOLOv8 variants tested, the Medium model achieved the highest performance, with
a mean Average Precision at 50% Intersection over Union (mAP50) exceeding 85%
for Ki-67-positive cells. The proposed approach offers an efficient, scalable,
and objective alternative to conventional scoring methods, supporting greater
consistency in Ki-67 evaluation. Future directions include developing
user-friendly clinical interfaces and expanding to multi-institutional datasets
to enhance generalizability and facilitate broader adoption in diagnostic
practice.
| no_new_dataset | 0.946745 |
2503.19625 | Xiangting Meng | Xiangting Meng, Jiaqi Yang, Mingshu Chen, Chenxin Yan, Yujiao Shi,
Wenchao Ding, and Laurent Kneip | DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and
Tracking in Moving Camera Scenarios | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | In the realm of object pose estimation, scenarios involving both dynamic
objects and moving cameras are prevalent. However, the scarcity of
corresponding real-world datasets significantly hinders the development and
evaluation of robust pose estimation models. This is largely attributed to the
inherent challenges in accurately annotating object poses in dynamic scenes
captured by moving cameras. To bridge this gap, this paper presents a novel
dataset DynOPETs and a dedicated data acquisition and annotation pipeline
tailored for object pose estimation and tracking in such unconstrained
environments. Our efficient annotation method innovatively integrates pose
estimation and pose tracking techniques to generate pseudo-labels, which are
subsequently refined through pose graph optimization. The resulting dataset
offers accurate pose annotations for dynamic objects observed from moving
cameras. To validate the effectiveness and value of our dataset, we perform
comprehensive evaluations using 18 state-of-the-art methods, demonstrating its
potential to accelerate research in this challenging domain. The dataset will
be made publicly available to facilitate further exploration and advancement in
the field.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:13:44 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Meng",
"Xiangting",
""
],
[
"Yang",
"Jiaqi",
""
],
[
"Chen",
"Mingshu",
""
],
[
"Yan",
"Chenxin",
""
],
[
"Shi",
"Yujiao",
""
],
[
"Ding",
"Wenchao",
""
],
[
"Kneip",
"Laurent",
""
]
] | TITLE: DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and
Tracking in Moving Camera Scenarios
ABSTRACT: In the realm of object pose estimation, scenarios involving both dynamic
objects and moving cameras are prevalent. However, the scarcity of
corresponding real-world datasets significantly hinders the development and
evaluation of robust pose estimation models. This is largely attributed to the
inherent challenges in accurately annotating object poses in dynamic scenes
captured by moving cameras. To bridge this gap, this paper presents a novel
dataset DynOPETs and a dedicated data acquisition and annotation pipeline
tailored for object pose estimation and tracking in such unconstrained
environments. Our efficient annotation method innovatively integrates pose
estimation and pose tracking techniques to generate pseudo-labels, which are
subsequently refined through pose graph optimization. The resulting dataset
offers accurate pose annotations for dynamic objects observed from moving
cameras. To validate the effectiveness and value of our dataset, we perform
comprehensive evaluations using 18 state-of-the-art methods, demonstrating its
potential to accelerate research in this challenging domain. The dataset will
be made publicly available to facilitate further exploration and advancement in
the field.
| new_dataset | 0.964052 |
2503.19633 | Yunjie Ji | Han Zhao, Haotian Wang, Yiping Peng, Sitong Zhao, Xiaoyu Tian,
Shuaiting Chen, Yunjie Ji, Xiangang Li | 1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large
Language Model Training | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces
for general reasoning tasks, composed of high-quality and challenging reasoning
problems. These problems are collected from a multitude of open-source
datasets, subjected to semantic deduplication and meticulous cleaning to
eliminate test set contamination. All responses within the dataset are
distilled from reasoning models (predominantly DeepSeek-R1) and have undergone
rigorous verification procedures. Mathematical problems are validated by
checking against reference answers, code problems are verified using test
cases, and other tasks are evaluated with the aid of a reward model. The
AM-Distill-Qwen-32B model, which was trained through only simple Supervised
Fine-Tuning (SFT) using this batch of data, outperformed the
DeepSeek-R1-Distill-Qwen-32B model on four benchmarks: AIME2024, MATH-500,
GPQA-Diamond, and LiveCodeBench. Additionally, the AM-Distill-Qwen-72B model
surpassed the DeepSeek-R1-Distill-Llama-70B model on all benchmarks as well. We
are releasing these 1.4 million problems and their corresponding responses to
the research community with the objective of fostering the development of
powerful reasoning-oriented Large Language Models (LLMs). The dataset was
published in
\href{https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M}{https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M}.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:19:46 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhao",
"Han",
""
],
[
"Wang",
"Haotian",
""
],
[
"Peng",
"Yiping",
""
],
[
"Zhao",
"Sitong",
""
],
[
"Tian",
"Xiaoyu",
""
],
[
"Chen",
"Shuaiting",
""
],
[
"Ji",
"Yunjie",
""
],
[
"Li",
"Xiangang",
""
]
] | TITLE: 1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large
Language Model Training
ABSTRACT: The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces
for general reasoning tasks, composed of high-quality and challenging reasoning
problems. These problems are collected from a multitude of open-source
datasets, subjected to semantic deduplication and meticulous cleaning to
eliminate test set contamination. All responses within the dataset are
distilled from reasoning models (predominantly DeepSeek-R1) and have undergone
rigorous verification procedures. Mathematical problems are validated by
checking against reference answers, code problems are verified using test
cases, and other tasks are evaluated with the aid of a reward model. The
AM-Distill-Qwen-32B model, which was trained through only simple Supervised
Fine-Tuning (SFT) using this batch of data, outperformed the
DeepSeek-R1-Distill-Qwen-32B model on four benchmarks: AIME2024, MATH-500,
GPQA-Diamond, and LiveCodeBench. Additionally, the AM-Distill-Qwen-72B model
surpassed the DeepSeek-R1-Distill-Llama-70B model on all benchmarks as well. We
are releasing these 1.4 million problems and their corresponding responses to
the research community with the objective of fostering the development of
powerful reasoning-oriented Large Language Models (LLMs). The dataset was
published in
\href{https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M}{https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M}.
| new_dataset | 0.796846 |
2503.19647 | Niccolo Avogaro | Niccolo Avogaro, Thomas Frick, Mattia Rigotti, Andrea Bartezzaghi,
Filip Janicki, Cristiano Malossi, Konrad Schindler, Roy Assaf | Show or Tell? Effectively prompting Vision-Language Models for semantic
segmentation | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Vision-Language Models (VLMs) are increasingly being regarded as
foundation models that can be instructed to solve diverse tasks by prompting,
without task-specific training. We examine the seemingly obvious question: how
to effectively prompt VLMs for semantic segmentation. To that end, we
systematically evaluate the segmentation performance of several recent models
guided by either text or visual prompts on the out-of-distribution MESS dataset
collection. We introduce a scalable prompting scheme, few-shot prompted
semantic segmentation, inspired by open-vocabulary segmentation and few-shot
learning. It turns out that VLMs lag far behind specialist models trained for a
specific segmentation task, by about 30% on average on the
Intersection-over-Union metric. Moreover, we find that text prompts and visual
prompts are complementary: each one of the two modes fails on many examples
that the other one can solve. Our analysis suggests that being able to
anticipate the most effective prompt modality can lead to a 11% improvement in
performance. Motivated by our findings, we propose PromptMatcher, a remarkably
simple training-free baseline that combines both text and visual prompts,
achieving state-of-the-art results outperforming the best text-prompted VLM by
2.5%, and the top visual-prompted VLM by 3.5% on few-shot prompted semantic
segmentation.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:36:59 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Avogaro",
"Niccolo",
""
],
[
"Frick",
"Thomas",
""
],
[
"Rigotti",
"Mattia",
""
],
[
"Bartezzaghi",
"Andrea",
""
],
[
"Janicki",
"Filip",
""
],
[
"Malossi",
"Cristiano",
""
],
[
"Schindler",
"Konrad",
""
],
[
"Assaf",
"Roy",
""
]
] | TITLE: Show or Tell? Effectively prompting Vision-Language Models for semantic
segmentation
ABSTRACT: Large Vision-Language Models (VLMs) are increasingly being regarded as
foundation models that can be instructed to solve diverse tasks by prompting,
without task-specific training. We examine the seemingly obvious question: how
to effectively prompt VLMs for semantic segmentation. To that end, we
systematically evaluate the segmentation performance of several recent models
guided by either text or visual prompts on the out-of-distribution MESS dataset
collection. We introduce a scalable prompting scheme, few-shot prompted
semantic segmentation, inspired by open-vocabulary segmentation and few-shot
learning. It turns out that VLMs lag far behind specialist models trained for a
specific segmentation task, by about 30% on average on the
Intersection-over-Union metric. Moreover, we find that text prompts and visual
prompts are complementary: each one of the two modes fails on many examples
that the other one can solve. Our analysis suggests that being able to
anticipate the most effective prompt modality can lead to a 11% improvement in
performance. Motivated by our findings, we propose PromptMatcher, a remarkably
simple training-free baseline that combines both text and visual prompts,
achieving state-of-the-art results outperforming the best text-prompted VLM by
2.5%, and the top visual-prompted VLM by 3.5% on few-shot prompted semantic
segmentation.
| no_new_dataset | 0.951006 |
2503.19649 | Yuanyuan Zhang | Yuanyuan Zhang, Sijie Xiong, Rui Yang, EngGee Lim, Yutao Yue | Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac
Feature Monitoring from Radar Signal Components | null | null | null | null | eess.SP cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Radar-based wellness monitoring is becoming an effective measurement to
provide accurate vital signs in a contactless manner, but data scarcity retards
the related research on deep-learning-based methods. Data augmentation is
commonly used to enrich the dataset by modifying the existing data, but most
augmentation techniques can only couple with classification tasks. To enable
the augmentation for regression tasks, this research proposes a spectrogram
augmentation method, Horcrux, for radar-based cardiac feature monitoring (e.g.,
heartbeat detection, electrocardiogram reconstruction) with both classification
and regression tasks involved. The proposed method is designed to increase the
diversity of input samples while the augmented spectrogram is still faithful to
the original ground truth vital sign. In addition, Horcrux proposes to inject
zero values in specific areas to enhance the awareness of the deep learning
model on subtle cardiac features, improving the performance for the limited
dataset. Experimental result shows that Horcrux achieves an overall improvement
of 16.20% in cardiac monitoring and has the potential to be extended to other
spectrogram-based tasks. The code will be released upon publication.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:40:05 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zhang",
"Yuanyuan",
""
],
[
"Xiong",
"Sijie",
""
],
[
"Yang",
"Rui",
""
],
[
"Lim",
"EngGee",
""
],
[
"Yue",
"Yutao",
""
]
] | TITLE: Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac
Feature Monitoring from Radar Signal Components
ABSTRACT: Radar-based wellness monitoring is becoming an effective measurement to
provide accurate vital signs in a contactless manner, but data scarcity retards
the related research on deep-learning-based methods. Data augmentation is
commonly used to enrich the dataset by modifying the existing data, but most
augmentation techniques can only couple with classification tasks. To enable
the augmentation for regression tasks, this research proposes a spectrogram
augmentation method, Horcrux, for radar-based cardiac feature monitoring (e.g.,
heartbeat detection, electrocardiogram reconstruction) with both classification
and regression tasks involved. The proposed method is designed to increase the
diversity of input samples while the augmented spectrogram is still faithful to
the original ground truth vital sign. In addition, Horcrux proposes to inject
zero values in specific areas to enhance the awareness of the deep learning
model on subtle cardiac features, improving the performance for the limited
dataset. Experimental result shows that Horcrux achieves an overall improvement
of 16.20% in cardiac monitoring and has the potential to be extended to other
spectrogram-based tasks. The code will be released upon publication.
| no_new_dataset | 0.948537 |
2503.19650 | Ibrahim Said Ahmad | Maryam Bala, Amina Imam Abubakar, Abdulhamid Abubakar, Abdulkadir
Shehu Bichi, Hafsa Kabir Ahmad, Sani Abdullahi Sani, Idris Abdulmumin,
Shamsuddeen Hassan Muhamad, Ibrahim Said Ahmad | HausaNLP at SemEval-2025 Task 3: Towards a Fine-Grained Model-Aware
Hallucination Detection | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | This paper presents our findings of the Multilingual Shared Task on
Hallucinations and Related Observable Overgeneration Mistakes, MU-SHROOM, which
focuses on identifying hallucinations and related overgeneration errors in
large language models (LLMs). The shared task involves detecting specific text
spans that constitute hallucinations in the outputs generated by LLMs in 14
languages. To address this task, we aim to provide a nuanced, model-aware
understanding of hallucination occurrences and severity in English. We used
natural language inference and fine-tuned a ModernBERT model using a synthetic
dataset of 400 samples, achieving an Intersection over Union (IoU) score of
0.032 and a correlation score of 0.422. These results indicate a moderately
positive correlation between the model's confidence scores and the actual
presence of hallucinations. The IoU score indicates that our model has a
relatively low overlap between the predicted hallucination span and the truth
annotation. The performance is unsurprising, given the intricate nature of
hallucination detection. Hallucinations often manifest subtly, relying on
context, making pinpointing their exact boundaries formidable.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:40:22 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Bala",
"Maryam",
""
],
[
"Abubakar",
"Amina Imam",
""
],
[
"Abubakar",
"Abdulhamid",
""
],
[
"Bichi",
"Abdulkadir Shehu",
""
],
[
"Ahmad",
"Hafsa Kabir",
""
],
[
"Sani",
"Sani Abdullahi",
""
],
[
"Abdulmumin",
"Idris",
""
],
[
"Muhamad",
"Shamsuddeen Hassan",
""
],
[
"Ahmad",
"Ibrahim Said",
""
]
] | TITLE: HausaNLP at SemEval-2025 Task 3: Towards a Fine-Grained Model-Aware
Hallucination Detection
ABSTRACT: This paper presents our findings of the Multilingual Shared Task on
Hallucinations and Related Observable Overgeneration Mistakes, MU-SHROOM, which
focuses on identifying hallucinations and related overgeneration errors in
large language models (LLMs). The shared task involves detecting specific text
spans that constitute hallucinations in the outputs generated by LLMs in 14
languages. To address this task, we aim to provide a nuanced, model-aware
understanding of hallucination occurrences and severity in English. We used
natural language inference and fine-tuned a ModernBERT model using a synthetic
dataset of 400 samples, achieving an Intersection over Union (IoU) score of
0.032 and a correlation score of 0.422. These results indicate a moderately
positive correlation between the model's confidence scores and the actual
presence of hallucinations. The IoU score indicates that our model has a
relatively low overlap between the predicted hallucination span and the truth
annotation. The performance is unsurprising, given the intricate nature of
hallucination detection. Hallucinations often manifest subtly, relying on
context, making pinpointing their exact boundaries formidable.
| new_dataset | 0.949342 |
2503.19658 | Jan Koh\'ut | Jan Koh\'ut, Martin Do\v{c}ekal, Michal Hradi\v{s}, Marek Va\v{s}ko | BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata
Extraction | Submitted to ICDAR2025 conference | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Manual digitization of bibliographic metadata is time consuming and labor
intensive, especially for historical and real-world archives with highly
variable formatting across documents. Despite advances in machine learning, the
absence of dedicated datasets for metadata extraction hinders automation. To
address this gap, we introduce BiblioPage, a dataset of scanned title pages
annotated with structured bibliographic metadata. The dataset consists of
approximately 2,000 monograph title pages collected from 14 Czech libraries,
spanning a wide range of publication periods, typographic styles, and layout
structures. Each title page is annotated with 16 bibliographic attributes,
including title, contributors, and publication metadata, along with precise
positional information in the form of bounding boxes. To extract structured
information from this dataset, we valuated object detection models such as YOLO
and DETR combined with transformer-based OCR, achieving a maximum mAP of 52 and
an F1 score of 59. Additionally, we assess the performance of various visual
large language models, including LlamA 3.2-Vision and GPT-4o, with the best
model reaching an F1 score of 67. BiblioPage serves as a real-world benchmark
for bibliographic metadata extraction, contributing to document understanding,
document question answering, and document information extraction. Dataset and
evaluation scripts are availible at: https://github.com/DCGM/biblio-dataset
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:46:55 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Kohút",
"Jan",
""
],
[
"Dočekal",
"Martin",
""
],
[
"Hradiš",
"Michal",
""
],
[
"Vaško",
"Marek",
""
]
] | TITLE: BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata
Extraction
ABSTRACT: Manual digitization of bibliographic metadata is time consuming and labor
intensive, especially for historical and real-world archives with highly
variable formatting across documents. Despite advances in machine learning, the
absence of dedicated datasets for metadata extraction hinders automation. To
address this gap, we introduce BiblioPage, a dataset of scanned title pages
annotated with structured bibliographic metadata. The dataset consists of
approximately 2,000 monograph title pages collected from 14 Czech libraries,
spanning a wide range of publication periods, typographic styles, and layout
structures. Each title page is annotated with 16 bibliographic attributes,
including title, contributors, and publication metadata, along with precise
positional information in the form of bounding boxes. To extract structured
information from this dataset, we valuated object detection models such as YOLO
and DETR combined with transformer-based OCR, achieving a maximum mAP of 52 and
an F1 score of 59. Additionally, we assess the performance of various visual
large language models, including LlamA 3.2-Vision and GPT-4o, with the best
model reaching an F1 score of 67. BiblioPage serves as a real-world benchmark
for bibliographic metadata extraction, contributing to document understanding,
document question answering, and document information extraction. Dataset and
evaluation scripts are availible at: https://github.com/DCGM/biblio-dataset
| new_dataset | 0.965218 |
2503.19661 | Chinedu Nwoye | Rupak Bose, Chinedu Innocent Nwoye, Aditya Bhat, Nicolas Padoy | CoSimGen: Controllable Diffusion Model for Simultaneous Image and Mask
Generation | 15 pages, 14 figure, 2 tables, project page at
https://camma-public.github.io/endogen/cosimgen | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The acquisition of annotated datasets with paired images and segmentation
masks is a critical challenge in domains such as medical imaging, remote
sensing, and computer vision. Manual annotation demands significant resources,
faces ethical constraints, and depends heavily on domain expertise. Existing
generative models often target single-modality outputs, either images or
segmentation masks, failing to address the need for high-quality, simultaneous
image-mask generation. Additionally, these models frequently lack adaptable
conditioning mechanisms, restricting control over the generated outputs and
limiting their applicability for dataset augmentation and rare scenario
simulation. We propose CoSimGen, a diffusion-based framework for controllable
simultaneous image and mask generation. Conditioning is intuitively achieved
through (1) text prompts grounded in class semantics, (2) spatial embedding of
context prompts to provide spatial coherence, and (3) spectral embedding of
timestep information to model noise levels during diffusion. To enhance
controllability and training efficiency, the framework incorporates contrastive
triplet loss between text and class embeddings, alongside diffusion and
adversarial losses. Initial low-resolution outputs 128 x 128 are super-resolved
to 512 x 512, producing high-fidelity images and masks with strict adherence to
conditions. We evaluate CoSimGen on metrics such as FID, KID, LPIPS, Class FID,
Positive predicted value for image fidelity and semantic alignment of generated
samples over 4 diverse datasets. CoSimGen achieves state-of-the-art performance
across all datasets, achieving the lowest KID of 0.11 and LPIPS of 0.53 across
datasets.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:48:22 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Bose",
"Rupak",
""
],
[
"Nwoye",
"Chinedu Innocent",
""
],
[
"Bhat",
"Aditya",
""
],
[
"Padoy",
"Nicolas",
""
]
] | TITLE: CoSimGen: Controllable Diffusion Model for Simultaneous Image and Mask
Generation
ABSTRACT: The acquisition of annotated datasets with paired images and segmentation
masks is a critical challenge in domains such as medical imaging, remote
sensing, and computer vision. Manual annotation demands significant resources,
faces ethical constraints, and depends heavily on domain expertise. Existing
generative models often target single-modality outputs, either images or
segmentation masks, failing to address the need for high-quality, simultaneous
image-mask generation. Additionally, these models frequently lack adaptable
conditioning mechanisms, restricting control over the generated outputs and
limiting their applicability for dataset augmentation and rare scenario
simulation. We propose CoSimGen, a diffusion-based framework for controllable
simultaneous image and mask generation. Conditioning is intuitively achieved
through (1) text prompts grounded in class semantics, (2) spatial embedding of
context prompts to provide spatial coherence, and (3) spectral embedding of
timestep information to model noise levels during diffusion. To enhance
controllability and training efficiency, the framework incorporates contrastive
triplet loss between text and class embeddings, alongside diffusion and
adversarial losses. Initial low-resolution outputs 128 x 128 are super-resolved
to 512 x 512, producing high-fidelity images and masks with strict adherence to
conditions. We evaluate CoSimGen on metrics such as FID, KID, LPIPS, Class FID,
Positive predicted value for image fidelity and semantic alignment of generated
samples over 4 diverse datasets. CoSimGen achieves state-of-the-art performance
across all datasets, achieving the lowest KID of 0.11 and LPIPS of 0.53 across
datasets.
| no_new_dataset | 0.950319 |
2503.19668 | Fabio Martinez Carrillo | Fredy Alejandro Mendoza L\'opez, Jefferson Rodriguez, Fabio Mart\'inez | A multitask transformer to sign language translation using motion
gesture primitives | 32 pages, 10 tables, 13 figures | null | null | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | The absence of effective communication the deaf population represents the
main social gap in this community. Furthermore, the sign language, main deaf
communication tool, is unlettered, i.e., there is no formal written
representation. In consequence, main challenge today is the automatic
translation among spatiotemporal sign representation and natural text language.
Recent approaches are based on encoder-decoder architectures, where the most
relevant strategies integrate attention modules to enhance non-linear
correspondences, besides, many of these approximations require complex training
and architectural schemes to achieve reasonable predictions, because of the
absence of intermediate text projections. However, they are still limited by
the redundant background information of the video sequences. This work
introduces a multitask transformer architecture that includes a gloss learning
representation to achieve a more suitable translation. The proposed approach
also includes a dense motion representation that enhances gestures and includes
kinematic information, a key component in sign language. From this
representation it is possible to avoid background information and exploit the
geometry of the signs, in addition, it includes spatiotemporal representations
that facilitate the alignment between gestures and glosses as an intermediate
textual representation. The proposed approach outperforms the state-of-the-art
evaluated on the CoL-SLTD dataset, achieving a BLEU-4 of 72,64% in split 1, and
a BLEU-4 of 14,64% in split 2. Additionally, the strategy was validated on the
RWTH-PHOENIX-Weather 2014 T dataset, achieving a competitive BLEU-4 of 11,58%.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 13:53:25 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"López",
"Fredy Alejandro Mendoza",
""
],
[
"Rodriguez",
"Jefferson",
""
],
[
"Martínez",
"Fabio",
""
]
] | TITLE: A multitask transformer to sign language translation using motion
gesture primitives
ABSTRACT: The absence of effective communication the deaf population represents the
main social gap in this community. Furthermore, the sign language, main deaf
communication tool, is unlettered, i.e., there is no formal written
representation. In consequence, main challenge today is the automatic
translation among spatiotemporal sign representation and natural text language.
Recent approaches are based on encoder-decoder architectures, where the most
relevant strategies integrate attention modules to enhance non-linear
correspondences, besides, many of these approximations require complex training
and architectural schemes to achieve reasonable predictions, because of the
absence of intermediate text projections. However, they are still limited by
the redundant background information of the video sequences. This work
introduces a multitask transformer architecture that includes a gloss learning
representation to achieve a more suitable translation. The proposed approach
also includes a dense motion representation that enhances gestures and includes
kinematic information, a key component in sign language. From this
representation it is possible to avoid background information and exploit the
geometry of the signs, in addition, it includes spatiotemporal representations
that facilitate the alignment between gestures and glosses as an intermediate
textual representation. The proposed approach outperforms the state-of-the-art
evaluated on the CoL-SLTD dataset, achieving a BLEU-4 of 72,64% in split 1, and
a BLEU-4 of 14,64% in split 2. Additionally, the strategy was validated on the
RWTH-PHOENIX-Weather 2014 T dataset, achieving a competitive BLEU-4 of 11,58%.
| no_new_dataset | 0.942507 |
2503.19673 | Federico Lincetto | Federico Lincetto, Gianluca Agresti, Mattia Rossi, Pietro Zanuttigh | MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for
Neural Rendering across Multiple Imaging Modalities | Accepted at CVPR 2025 | null | null | null | cs.GR cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Neural Radiance Fields (NeRF) have shown impressive performances in the
rendering of 3D scenes from arbitrary viewpoints. While RGB images are widely
preferred for training volume rendering models, the interest in other radiance
modalities is also growing. However, the capability of the underlying implicit
neural models to learn and transfer information across heterogeneous imaging
modalities has seldom been explored, mostly due to the limited training data
availability. For this purpose, we present MultimodalStudio (MMS): it
encompasses MMS-DATA and MMS-FW. MMS-DATA is a multimodal multi-view dataset
containing 32 scenes acquired with 5 different imaging modalities: RGB,
monochrome, near-infrared, polarization and multispectral. MMS-FW is a novel
modular multimodal NeRF framework designed to handle multimodal raw data and
able to support an arbitrary number of multi-channel devices. Through extensive
experiments, we demonstrate that MMS-FW trained on MMS-DATA can transfer
information between different imaging modalities and produce higher quality
renderings than using single modalities alone. We publicly release the dataset
and the framework, to promote the research on multimodal volume rendering and
beyond.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 14:00:11 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Lincetto",
"Federico",
""
],
[
"Agresti",
"Gianluca",
""
],
[
"Rossi",
"Mattia",
""
],
[
"Zanuttigh",
"Pietro",
""
]
] | TITLE: MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for
Neural Rendering across Multiple Imaging Modalities
ABSTRACT: Neural Radiance Fields (NeRF) have shown impressive performances in the
rendering of 3D scenes from arbitrary viewpoints. While RGB images are widely
preferred for training volume rendering models, the interest in other radiance
modalities is also growing. However, the capability of the underlying implicit
neural models to learn and transfer information across heterogeneous imaging
modalities has seldom been explored, mostly due to the limited training data
availability. For this purpose, we present MultimodalStudio (MMS): it
encompasses MMS-DATA and MMS-FW. MMS-DATA is a multimodal multi-view dataset
containing 32 scenes acquired with 5 different imaging modalities: RGB,
monochrome, near-infrared, polarization and multispectral. MMS-FW is a novel
modular multimodal NeRF framework designed to handle multimodal raw data and
able to support an arbitrary number of multi-channel devices. Through extensive
experiments, we demonstrate that MMS-FW trained on MMS-DATA can transfer
information between different imaging modalities and produce higher quality
renderings than using single modalities alone. We publicly release the dataset
and the framework, to promote the research on multimodal volume rendering and
beyond.
| new_dataset | 0.957952 |
2503.19689 | Uttam Cadambi Padmanaban | Uttam Cadambi Padmanaban, Bharathram Ganapathisubramani, Sean Symon | Three-dimensional variational data assimilation of separated flows using
time-averaged experimental data | 47 pages, 23 figures | null | null | null | physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | We present a novel framework for assimilating planar PIV experimental data
using a variational approach to enhance the predictions of the Spalart-Allmaras
RANS turbulence model. Our method applies three-dimensional constraints to the
assimilation of mean velocity data, incorporating a corrective forcing term in
the momentum equations. The advantages of this approach are highlighted through
a direct comparison with traditional two-dimensional assimilation using the
same experimental dataset. We demonstrate its efficacy by assimilating the deep
stall flow over a NACA0012 airfoil at a $15^\circ$ angle of attack and a
chord-based Reynolds number of $Re_c \approx 7.5 \times 10^4$. We find that in
two-dimensional assimilation, the corrective forcing term compensates not only
for physical modeling errors but also for the lack of divergence in the
experimental data. This conflation makes it difficult to isolate the effects of
measurement inconsistencies from deficiencies in the turbulence model. In
contrast, three-dimensional assimilation allows the corrective forcing term to
primarily address experimental setup errors while enabling the turbulence model
to more accurately capture the flow physics. We establish the superiority of
three-dimensional assimilation by demonstrating improved agreement in
reconstructed quantities, including pressure, lift force, and Reynolds shear
stress.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 14:16:50 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Padmanaban",
"Uttam Cadambi",
""
],
[
"Ganapathisubramani",
"Bharathram",
""
],
[
"Symon",
"Sean",
""
]
] | TITLE: Three-dimensional variational data assimilation of separated flows using
time-averaged experimental data
ABSTRACT: We present a novel framework for assimilating planar PIV experimental data
using a variational approach to enhance the predictions of the Spalart-Allmaras
RANS turbulence model. Our method applies three-dimensional constraints to the
assimilation of mean velocity data, incorporating a corrective forcing term in
the momentum equations. The advantages of this approach are highlighted through
a direct comparison with traditional two-dimensional assimilation using the
same experimental dataset. We demonstrate its efficacy by assimilating the deep
stall flow over a NACA0012 airfoil at a $15^\circ$ angle of attack and a
chord-based Reynolds number of $Re_c \approx 7.5 \times 10^4$. We find that in
two-dimensional assimilation, the corrective forcing term compensates not only
for physical modeling errors but also for the lack of divergence in the
experimental data. This conflation makes it difficult to isolate the effects of
measurement inconsistencies from deficiencies in the turbulence model. In
contrast, three-dimensional assimilation allows the corrective forcing term to
primarily address experimental setup errors while enabling the turbulence model
to more accurately capture the flow physics. We establish the superiority of
three-dimensional assimilation by demonstrating improved agreement in
reconstructed quantities, including pressure, lift force, and Reynolds shear
stress.
| no_new_dataset | 0.948775 |
2503.19707 | Ilias Marios Stogiannidis | Ilias Stogiannidis, Steven McDonagh, Sotirios A. Tsaftaris | Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models | 8 main pages, 4 pages Appendix, 5 figures | null | null | null | cs.CV cs.CL | http://creativecommons.org/licenses/by/4.0/ | Vision-Language Models (VLMs) have recently emerged as powerful tools,
excelling in tasks that integrate visual and textual comprehension, such as
image captioning, visual question answering, and image-text retrieval. However,
existing benchmarks for VLMs include spatial components, which often fail to
isolate spatial reasoning from related tasks such as object detection or
semantic comprehension. In this paper, we address these deficiencies with a
multi-faceted approach towards understanding spatial reasoning. Informed by the
diverse and multi-dimensional nature of human spatial reasoning abilities, we
present a detailed analysis that first delineates the core elements of spatial
reasoning: spatial relations, orientation and navigation, mental rotation, and
spatial visualization, and then assesses the performance of these models in
both synthetic and real-world images, bridging controlled and naturalistic
contexts. We analyze 13 state-of-the-art Vision-Language Models, uncovering
pivotal insights into their spatial reasoning performance. Our results reveal
profound shortcomings in current VLMs, with average accuracy across the 13
models approximating random chance, highlighting spatial reasoning as a
persistent obstacle. This work not only exposes the pressing need to advance
spatial reasoning within VLMs but also establishes a solid platform for future
exploration. Code available on GitHub (https://github.com/stogiannidis/srbench)
and dataset available on HuggingFace
(https://huggingface.co/datasets/stogiannidis/srbench).
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 14:34:06 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Stogiannidis",
"Ilias",
""
],
[
"McDonagh",
"Steven",
""
],
[
"Tsaftaris",
"Sotirios A.",
""
]
] | TITLE: Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
ABSTRACT: Vision-Language Models (VLMs) have recently emerged as powerful tools,
excelling in tasks that integrate visual and textual comprehension, such as
image captioning, visual question answering, and image-text retrieval. However,
existing benchmarks for VLMs include spatial components, which often fail to
isolate spatial reasoning from related tasks such as object detection or
semantic comprehension. In this paper, we address these deficiencies with a
multi-faceted approach towards understanding spatial reasoning. Informed by the
diverse and multi-dimensional nature of human spatial reasoning abilities, we
present a detailed analysis that first delineates the core elements of spatial
reasoning: spatial relations, orientation and navigation, mental rotation, and
spatial visualization, and then assesses the performance of these models in
both synthetic and real-world images, bridging controlled and naturalistic
contexts. We analyze 13 state-of-the-art Vision-Language Models, uncovering
pivotal insights into their spatial reasoning performance. Our results reveal
profound shortcomings in current VLMs, with average accuracy across the 13
models approximating random chance, highlighting spatial reasoning as a
persistent obstacle. This work not only exposes the pressing need to advance
spatial reasoning within VLMs but also establishes a solid platform for future
exploration. Code available on GitHub (https://github.com/stogiannidis/srbench)
and dataset available on HuggingFace
(https://huggingface.co/datasets/stogiannidis/srbench).
| no_new_dataset | 0.838349 |
2503.19713 | Yusen Xie | Yusen Xie, Zhengmin Huang, Shaojie Shen, Jun Ma | Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras
for Autonomous Driving | null | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce Semi-SD, a novel metric depth estimation
framework tailored for surrounding cameras equipment in autonomous driving. In
this work, the input data consists of adjacent surrounding frames and camera
parameters. We propose a unified spatial-temporal-semantic fusion module to
construct the visual fused features. Cross-attention components for surrounding
cameras and adjacent frames are utilized to focus on metric scale information
refinement and temporal feature matching. Building on this, we propose a pose
estimation framework using surrounding cameras, their corresponding estimated
depths, and extrinsic parameters, which effectively address the scale ambiguity
in multi-camera setups. Moreover, semantic world model and monocular depth
estimation world model are integrated to supervised the depth estimation, which
improve the quality of depth estimation. We evaluate our algorithm on DDAD and
nuScenes datasets, and the results demonstrate that our method achieves
state-of-the-art performance in terms of surrounding camera based depth
estimation quality. The source code will be available on
https://github.com/xieyuser/Semi-SD.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 14:39:04 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Xie",
"Yusen",
""
],
[
"Huang",
"Zhengmin",
""
],
[
"Shen",
"Shaojie",
""
],
[
"Ma",
"Jun",
""
]
] | TITLE: Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras
for Autonomous Driving
ABSTRACT: In this paper, we introduce Semi-SD, a novel metric depth estimation
framework tailored for surrounding cameras equipment in autonomous driving. In
this work, the input data consists of adjacent surrounding frames and camera
parameters. We propose a unified spatial-temporal-semantic fusion module to
construct the visual fused features. Cross-attention components for surrounding
cameras and adjacent frames are utilized to focus on metric scale information
refinement and temporal feature matching. Building on this, we propose a pose
estimation framework using surrounding cameras, their corresponding estimated
depths, and extrinsic parameters, which effectively address the scale ambiguity
in multi-camera setups. Moreover, semantic world model and monocular depth
estimation world model are integrated to supervised the depth estimation, which
improve the quality of depth estimation. We evaluate our algorithm on DDAD and
nuScenes datasets, and the results demonstrate that our method achieves
state-of-the-art performance in terms of surrounding camera based depth
estimation quality. The source code will be available on
https://github.com/xieyuser/Semi-SD.
| no_new_dataset | 0.951051 |
2503.19735 | Zixue Zeng | Zixue Zeng, Matthew Cartier, Xiaoyan Zhao, Pengyu Chen, Xin Meng,
Zhiyu Sheng, Maryam Satarpour, John M Cormack, Allison C. Bean, Ryan P.
Nussbaum, Maya Maurer, Emily Landis-Walkenhorst, Kang Kim, Ajay D. Wasan,
Jiantao Pu | InterSliceBoost: Identifying Tissue Layers in Three-dimensional
Ultrasound Images for Chronic Lower Back Pain (cLBP) Assessment | null | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | Available studies on chronic lower back pain (cLBP) typically focus on one or
a few specific tissues rather than conducting a comprehensive layer-by-layer
analysis. Since three-dimensional (3-D) images often contain hundreds of
slices, manual annotation of these anatomical structures is both time-consuming
and error-prone. We aim to develop and validate a novel approach called
InterSliceBoost to enable the training of a segmentation model on a partially
annotated dataset without compromising segmentation performance. The
architecture of InterSliceBoost includes two components: an inter-slice
generator and a segmentation model. The generator utilizes residual block-based
encoders to extract features from adjacent image-mask pairs (IMPs).
Differential features are calculated and input into a decoder to generate
inter-slice IMPs. The segmentation model is trained on partially annotated
datasets (e.g., skipping 1, 2, 3, or 7 images) and the generated inter-slice
IMPs. To validate the performance of InterSliceBoost, we utilized a dataset of
76 B-mode ultrasound scans acquired on 29 subjects enrolled in an ongoing cLBP
study. InterSliceBoost, trained on only 33% of the image slices, achieved a
mean Dice coefficient of 80.84% across all six layers on the independent test
set, with Dice coefficients of 73.48%, 61.11%, 81.87%, 95.74%, 83.52% and
88.74% for segmenting dermis, superficial fat, superficial fascial membrane,
deep fat, deep fascial membrane, and muscle. This performance is significantly
higher than the conventional model trained on fully annotated images (p<0.05).
InterSliceBoost can effectively segment the six tissue layers depicted on 3-D
B-model ultrasound images in settings with partial annotations.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:02:23 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zeng",
"Zixue",
""
],
[
"Cartier",
"Matthew",
""
],
[
"Zhao",
"Xiaoyan",
""
],
[
"Chen",
"Pengyu",
""
],
[
"Meng",
"Xin",
""
],
[
"Sheng",
"Zhiyu",
""
],
[
"Satarpour",
"Maryam",
""
],
[
"Cormack",
"John M",
""
],
[
"Bean",
"Allison C.",
""
],
[
"Nussbaum",
"Ryan P.",
""
],
[
"Maurer",
"Maya",
""
],
[
"Landis-Walkenhorst",
"Emily",
""
],
[
"Kim",
"Kang",
""
],
[
"Wasan",
"Ajay D.",
""
],
[
"Pu",
"Jiantao",
""
]
] | TITLE: InterSliceBoost: Identifying Tissue Layers in Three-dimensional
Ultrasound Images for Chronic Lower Back Pain (cLBP) Assessment
ABSTRACT: Available studies on chronic lower back pain (cLBP) typically focus on one or
a few specific tissues rather than conducting a comprehensive layer-by-layer
analysis. Since three-dimensional (3-D) images often contain hundreds of
slices, manual annotation of these anatomical structures is both time-consuming
and error-prone. We aim to develop and validate a novel approach called
InterSliceBoost to enable the training of a segmentation model on a partially
annotated dataset without compromising segmentation performance. The
architecture of InterSliceBoost includes two components: an inter-slice
generator and a segmentation model. The generator utilizes residual block-based
encoders to extract features from adjacent image-mask pairs (IMPs).
Differential features are calculated and input into a decoder to generate
inter-slice IMPs. The segmentation model is trained on partially annotated
datasets (e.g., skipping 1, 2, 3, or 7 images) and the generated inter-slice
IMPs. To validate the performance of InterSliceBoost, we utilized a dataset of
76 B-mode ultrasound scans acquired on 29 subjects enrolled in an ongoing cLBP
study. InterSliceBoost, trained on only 33% of the image slices, achieved a
mean Dice coefficient of 80.84% across all six layers on the independent test
set, with Dice coefficients of 73.48%, 61.11%, 81.87%, 95.74%, 83.52% and
88.74% for segmenting dermis, superficial fat, superficial fascial membrane,
deep fat, deep fascial membrane, and muscle. This performance is significantly
higher than the conventional model trained on fully annotated images (p<0.05).
InterSliceBoost can effectively segment the six tissue layers depicted on 3-D
B-model ultrasound images in settings with partial annotations.
| no_new_dataset | 0.745028 |
2503.19736 | Zixue Zeng | Zixue Zeng, Xiaoyan Zhao, Matthew Cartier, Xin Meng, Jiantao Pu | GRN+: A Simplified Generative Reinforcement Network for Tissue Layer
Analysis in 3D Ultrasound Images for Chronic Low-back Pain | null | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | 3D ultrasound delivers high-resolution, real-time images of soft tissues,
which is essential for pain research. However, manually distinguishing various
tissues for quantitative analysis is labor-intensive. To streamline this
process, we developed and validated GRN+, a novel multi-model framework that
automates layer segmentation with minimal annotated data. GRN+ combines a
ResNet-based generator and a U-Net segmentation model. Through a method called
Segmentation-guided Enhancement (SGE), the generator produces new images and
matching masks under the guidance of the segmentation model, with its weights
adjusted according to the segmentation loss gradient. To prevent gradient
explosion and secure stable training, a two-stage backpropagation strategy was
implemented: the first stage propagates the segmentation loss through both the
generator and segmentation model, while the second stage concentrates on
optimizing the segmentation model alone, thereby refining mask prediction using
the generated images. Tested on 69 fully annotated 3D ultrasound scans from 29
subjects with six manually labeled tissue layers, GRN+ outperformed all other
semi-supervised methods in terms of the Dice coefficient using only 5% labeled
data, despite not using unlabeled data for unsupervised training. Additionally,
when applied to fully annotated datasets, GRN+ with SGE achieved a 2.16% higher
Dice coefficient while incurring lower computational costs compared to other
models. Overall, GRN+ provides accurate tissue segmentation while reducing both
computational expenses and the dependency on extensive annotations, making it
an effective tool for 3D ultrasound analysis in cLBP patients.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:03:11 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Zeng",
"Zixue",
""
],
[
"Zhao",
"Xiaoyan",
""
],
[
"Cartier",
"Matthew",
""
],
[
"Meng",
"Xin",
""
],
[
"Pu",
"Jiantao",
""
]
] | TITLE: GRN+: A Simplified Generative Reinforcement Network for Tissue Layer
Analysis in 3D Ultrasound Images for Chronic Low-back Pain
ABSTRACT: 3D ultrasound delivers high-resolution, real-time images of soft tissues,
which is essential for pain research. However, manually distinguishing various
tissues for quantitative analysis is labor-intensive. To streamline this
process, we developed and validated GRN+, a novel multi-model framework that
automates layer segmentation with minimal annotated data. GRN+ combines a
ResNet-based generator and a U-Net segmentation model. Through a method called
Segmentation-guided Enhancement (SGE), the generator produces new images and
matching masks under the guidance of the segmentation model, with its weights
adjusted according to the segmentation loss gradient. To prevent gradient
explosion and secure stable training, a two-stage backpropagation strategy was
implemented: the first stage propagates the segmentation loss through both the
generator and segmentation model, while the second stage concentrates on
optimizing the segmentation model alone, thereby refining mask prediction using
the generated images. Tested on 69 fully annotated 3D ultrasound scans from 29
subjects with six manually labeled tissue layers, GRN+ outperformed all other
semi-supervised methods in terms of the Dice coefficient using only 5% labeled
data, despite not using unlabeled data for unsupervised training. Additionally,
when applied to fully annotated datasets, GRN+ with SGE achieved a 2.16% higher
Dice coefficient while incurring lower computational costs compared to other
models. Overall, GRN+ provides accurate tissue segmentation while reducing both
computational expenses and the dependency on extensive annotations, making it
an effective tool for 3D ultrasound analysis in cLBP patients.
| no_new_dataset | 0.954351 |
2503.19740 | Chengan Che | Chengan Che, Chao Wang, Tom Vercauteren, Sophia Tsoka, Luis C.
Garcia-Peraza-Herrera | Surg-3M: A Dataset and Foundation Model for Perception in Surgical
Settings | 15 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advancements in computer-assisted surgical procedures heavily rely on
accurate visual data interpretation from camera systems used during surgeries.
Traditional open-access datasets focusing on surgical procedures are often
limited by their small size, typically consisting of fewer than 100 videos with
less than 100K images. To address these constraints, a new dataset called
Surg-3M has been compiled using a novel aggregation pipeline that collects
high-resolution videos from online sources. Featuring an extensive collection
of over 4K surgical videos and more than 3 million high-quality images from
multiple procedure types, Surg-3M offers a comprehensive resource surpassing
existing alternatives in size and scope, including two novel tasks. To
demonstrate the effectiveness of this dataset, we present SurgFM, a
self-supervised foundation model pretrained on Surg-3M that achieves impressive
results in downstream tasks such as surgical phase recognition, action
recognition, and tool presence detection. Combining key components from
ConvNeXt, DINO, and an innovative augmented distillation method, SurgFM
exhibits exceptional performance compared to specialist architectures across
various benchmarks. Our experimental results show that SurgFM outperforms
state-of-the-art models in multiple downstream tasks, including significant
gains in surgical phase recognition (+8.9pp, +4.7pp, and +3.9pp of Jaccard in
AutoLaparo, M2CAI16, and Cholec80), action recognition (+3.1pp of mAP in
CholecT50) and tool presence detection (+4.6pp of mAP in Cholec80). Moreover,
even when using only half of the data, SurgFM outperforms state-of-the-art
models in AutoLaparo and achieves state-of-the-art performance in Cholec80.
Both Surg-3M and SurgFM have significant potential to accelerate progress
towards developing autonomous robotic surgery systems.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:05:00 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Che",
"Chengan",
""
],
[
"Wang",
"Chao",
""
],
[
"Vercauteren",
"Tom",
""
],
[
"Tsoka",
"Sophia",
""
],
[
"Garcia-Peraza-Herrera",
"Luis C.",
""
]
] | TITLE: Surg-3M: A Dataset and Foundation Model for Perception in Surgical
Settings
ABSTRACT: Advancements in computer-assisted surgical procedures heavily rely on
accurate visual data interpretation from camera systems used during surgeries.
Traditional open-access datasets focusing on surgical procedures are often
limited by their small size, typically consisting of fewer than 100 videos with
less than 100K images. To address these constraints, a new dataset called
Surg-3M has been compiled using a novel aggregation pipeline that collects
high-resolution videos from online sources. Featuring an extensive collection
of over 4K surgical videos and more than 3 million high-quality images from
multiple procedure types, Surg-3M offers a comprehensive resource surpassing
existing alternatives in size and scope, including two novel tasks. To
demonstrate the effectiveness of this dataset, we present SurgFM, a
self-supervised foundation model pretrained on Surg-3M that achieves impressive
results in downstream tasks such as surgical phase recognition, action
recognition, and tool presence detection. Combining key components from
ConvNeXt, DINO, and an innovative augmented distillation method, SurgFM
exhibits exceptional performance compared to specialist architectures across
various benchmarks. Our experimental results show that SurgFM outperforms
state-of-the-art models in multiple downstream tasks, including significant
gains in surgical phase recognition (+8.9pp, +4.7pp, and +3.9pp of Jaccard in
AutoLaparo, M2CAI16, and Cholec80), action recognition (+3.1pp of mAP in
CholecT50) and tool presence detection (+4.6pp of mAP in Cholec80). Moreover,
even when using only half of the data, SurgFM outperforms state-of-the-art
models in AutoLaparo and achieves state-of-the-art performance in Cholec80.
Both Surg-3M and SurgFM have significant potential to accelerate progress
towards developing autonomous robotic surgery systems.
| new_dataset | 0.960547 |
2503.19755 | Diankun Zhang | Haoyu Fu, Diankun Zhang, Zongchuang Zhao, Jianfeng Cui, Dingkang
Liang, Chong Zhang, Dingyuan Zhang, Hongwei Xie, Bing Wang, Xiang Bai | ORION: A Holistic End-to-End Autonomous Driving Framework by
Vision-Language Instructed Action Generation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | End-to-end (E2E) autonomous driving methods still struggle to make correct
decisions in interactive closed-loop evaluation due to limited causal reasoning
capability. Current methods attempt to leverage the powerful understanding and
reasoning abilities of Vision-Language Models (VLMs) to resolve this dilemma.
However, the problem is still open that few VLMs for E2E methods perform well
in the closed-loop evaluation due to the gap between the semantic reasoning
space and the purely numerical trajectory output in the action space. To tackle
this issue, we propose ORION, a holistic E2E autonomous driving framework by
vision-language instructed action generation. ORION uniquely combines a
QT-Former to aggregate long-term history context, a Large Language Model (LLM)
for driving scenario reasoning, and a generative planner for precision
trajectory prediction. ORION further aligns the reasoning space and the action
space to implement a unified E2E optimization for both visual
question-answering (VQA) and planning tasks. Our method achieves an impressive
closed-loop performance of 77.74 Driving Score (DS) and 54.62% Success Rate
(SR) on the challenge Bench2Drive datasets, which outperforms state-of-the-art
(SOTA) methods by a large margin of 14.28 DS and 19.61% SR.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:18:43 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Fu",
"Haoyu",
""
],
[
"Zhang",
"Diankun",
""
],
[
"Zhao",
"Zongchuang",
""
],
[
"Cui",
"Jianfeng",
""
],
[
"Liang",
"Dingkang",
""
],
[
"Zhang",
"Chong",
""
],
[
"Zhang",
"Dingyuan",
""
],
[
"Xie",
"Hongwei",
""
],
[
"Wang",
"Bing",
""
],
[
"Bai",
"Xiang",
""
]
] | TITLE: ORION: A Holistic End-to-End Autonomous Driving Framework by
Vision-Language Instructed Action Generation
ABSTRACT: End-to-end (E2E) autonomous driving methods still struggle to make correct
decisions in interactive closed-loop evaluation due to limited causal reasoning
capability. Current methods attempt to leverage the powerful understanding and
reasoning abilities of Vision-Language Models (VLMs) to resolve this dilemma.
However, the problem is still open that few VLMs for E2E methods perform well
in the closed-loop evaluation due to the gap between the semantic reasoning
space and the purely numerical trajectory output in the action space. To tackle
this issue, we propose ORION, a holistic E2E autonomous driving framework by
vision-language instructed action generation. ORION uniquely combines a
QT-Former to aggregate long-term history context, a Large Language Model (LLM)
for driving scenario reasoning, and a generative planner for precision
trajectory prediction. ORION further aligns the reasoning space and the action
space to implement a unified E2E optimization for both visual
question-answering (VQA) and planning tasks. Our method achieves an impressive
closed-loop performance of 77.74 Driving Score (DS) and 54.62% Success Rate
(SR) on the challenge Bench2Drive datasets, which outperforms state-of-the-art
(SOTA) methods by a large margin of 14.28 DS and 19.61% SR.
| no_new_dataset | 0.949529 |
2503.19757 | Zhi Hou | Zhi Hou, Tianyi Zhang, Yuwen Xiong, Haonan Duan, Hengjun Pu, Ronglei
Tong, Chengyang Zhao, Xizhou Zhu, Yu Qiao, Jifeng Dai, Yuntao Chen | Dita: Scaling Diffusion Transformer for Generalist
Vision-Language-Action Policy | Preprint; https://robodita.github.io; | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While recent vision-language-action models trained on diverse robot datasets
exhibit promising generalization capabilities with limited in-domain data,
their reliance on compact action heads to predict discretized or continuous
actions constrains adaptability to heterogeneous action spaces. We present
Dita, a scalable framework that leverages Transformer architectures to directly
denoise continuous action sequences through a unified multimodal diffusion
process. Departing from prior methods that condition denoising on fused
embeddings via shallow networks, Dita employs in-context conditioning --
enabling fine-grained alignment between denoised actions and raw visual tokens
from historical observations. This design explicitly models action deltas and
environmental nuances. By scaling the diffusion action denoiser alongside the
Transformer's scalability, Dita effectively integrates cross-embodiment
datasets across diverse camera perspectives, observation scenes, tasks, and
action spaces. Such synergy enhances robustness against various variances and
facilitates the successful execution of long-horizon tasks. Evaluations across
extensive benchmarks demonstrate state-of-the-art or comparative performance in
simulation. Notably, Dita achieves robust real-world adaptation to
environmental variances and complex long-horizon tasks through 10-shot
finetuning, using only third-person camera inputs. The architecture establishes
a versatile, lightweight and open-source baseline for generalist robot policy
learning. Project Page: https://robodita.github.io.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:19:56 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Hou",
"Zhi",
""
],
[
"Zhang",
"Tianyi",
""
],
[
"Xiong",
"Yuwen",
""
],
[
"Duan",
"Haonan",
""
],
[
"Pu",
"Hengjun",
""
],
[
"Tong",
"Ronglei",
""
],
[
"Zhao",
"Chengyang",
""
],
[
"Zhu",
"Xizhou",
""
],
[
"Qiao",
"Yu",
""
],
[
"Dai",
"Jifeng",
""
],
[
"Chen",
"Yuntao",
""
]
] | TITLE: Dita: Scaling Diffusion Transformer for Generalist
Vision-Language-Action Policy
ABSTRACT: While recent vision-language-action models trained on diverse robot datasets
exhibit promising generalization capabilities with limited in-domain data,
their reliance on compact action heads to predict discretized or continuous
actions constrains adaptability to heterogeneous action spaces. We present
Dita, a scalable framework that leverages Transformer architectures to directly
denoise continuous action sequences through a unified multimodal diffusion
process. Departing from prior methods that condition denoising on fused
embeddings via shallow networks, Dita employs in-context conditioning --
enabling fine-grained alignment between denoised actions and raw visual tokens
from historical observations. This design explicitly models action deltas and
environmental nuances. By scaling the diffusion action denoiser alongside the
Transformer's scalability, Dita effectively integrates cross-embodiment
datasets across diverse camera perspectives, observation scenes, tasks, and
action spaces. Such synergy enhances robustness against various variances and
facilitates the successful execution of long-horizon tasks. Evaluations across
extensive benchmarks demonstrate state-of-the-art or comparative performance in
simulation. Notably, Dita achieves robust real-world adaptation to
environmental variances and complex long-horizon tasks through 10-shot
finetuning, using only third-person camera inputs. The architecture establishes
a versatile, lightweight and open-source baseline for generalist robot policy
learning. Project Page: https://robodita.github.io.
| no_new_dataset | 0.947478 |
2503.19763 | Shuwei Li | Changhui Yuan, Shishun Zhao, Shuwei Li, Xinyuan Song, Zhao Chen | Interpretable Deep Regression Models with Interval-Censored Failure Time
Data | null | null | null | null | stat.ML cs.LG math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks (DNNs) have become powerful tools for modeling complex
data structures through sequentially integrating simple functions in each
hidden layer. In survival analysis, recent advances of DNNs primarily focus on
enhancing model capabilities, especially in exploring nonlinear covariate
effects under right censoring. However, deep learning methods for
interval-censored data, where the unobservable failure time is only known to
lie in an interval, remain underexplored and limited to specific data type or
model. This work proposes a general regression framework for interval-censored
data with a broad class of partially linear transformation models, where key
covariate effects are modeled parametrically while nonlinear effects of
nuisance multi-modal covariates are approximated via DNNs, balancing
interpretability and flexibility. We employ sieve maximum likelihood estimation
by leveraging monotone splines to approximate the cumulative baseline hazard
function. To ensure reliable and tractable estimation, we develop an EM
algorithm incorporating stochastic gradient descent. We establish the
asymptotic properties of parameter estimators and show that the DNN estimator
achieves minimax-optimal convergence. Extensive simulations demonstrate
superior estimation and prediction accuracy over state-of-the-art methods.
Applying our method to the Alzheimer's Disease Neuroimaging Initiative dataset
yields novel insights and improved predictive performance compared to
traditional approaches.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:27:32 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Yuan",
"Changhui",
""
],
[
"Zhao",
"Shishun",
""
],
[
"Li",
"Shuwei",
""
],
[
"Song",
"Xinyuan",
""
],
[
"Chen",
"Zhao",
""
]
] | TITLE: Interpretable Deep Regression Models with Interval-Censored Failure Time
Data
ABSTRACT: Deep neural networks (DNNs) have become powerful tools for modeling complex
data structures through sequentially integrating simple functions in each
hidden layer. In survival analysis, recent advances of DNNs primarily focus on
enhancing model capabilities, especially in exploring nonlinear covariate
effects under right censoring. However, deep learning methods for
interval-censored data, where the unobservable failure time is only known to
lie in an interval, remain underexplored and limited to specific data type or
model. This work proposes a general regression framework for interval-censored
data with a broad class of partially linear transformation models, where key
covariate effects are modeled parametrically while nonlinear effects of
nuisance multi-modal covariates are approximated via DNNs, balancing
interpretability and flexibility. We employ sieve maximum likelihood estimation
by leveraging monotone splines to approximate the cumulative baseline hazard
function. To ensure reliable and tractable estimation, we develop an EM
algorithm incorporating stochastic gradient descent. We establish the
asymptotic properties of parameter estimators and show that the DNN estimator
achieves minimax-optimal convergence. Extensive simulations demonstrate
superior estimation and prediction accuracy over state-of-the-art methods.
Applying our method to the Alzheimer's Disease Neuroimaging Initiative dataset
yields novel insights and improved predictive performance compared to
traditional approaches.
| no_new_dataset | 0.944177 |
2503.19769 | Suzhe Xu | Suzhe Xu, Jialin Peng, Chengyuan Zhang | BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection
between Point and Text Prompts | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Segmentation is a fundamental task in computer vision, with prompt-driven
methods gaining prominence due to their flexibility. The recent Segment
Anything Model (SAM) has demonstrated powerful point-prompt segmentation
capabilities, while text-based segmentation models offer rich semantic
understanding. However, existing approaches rarely explore how to effectively
combine these complementary modalities for optimal segmentation performance.
This paper presents BiPrompt-SAM, a novel dual-modal prompt segmentation
framework that fuses the advantages of point and text prompts through an
explicit selection mechanism. Specifically, we leverage SAM's inherent ability
to generate multiple mask candidates, combined with a semantic guidance mask
from text prompts, and explicitly select the most suitable candidate based on
similarity metrics. This approach can be viewed as a simplified Mixture of
Experts (MoE) system, where the point and text modules act as distinct
"experts," and the similarity scoring serves as a rudimentary "gating network."
We conducted extensive evaluations on both the Endovis17 medical dataset and
RefCOCO series natural image datasets. On Endovis17, BiPrompt-SAM achieved
89.55\% mDice and 81.46\% mIoU, comparable to state-of-the-art specialized
medical segmentation models. On the RefCOCO series datasets, our method
attained 87.1\%, 86.5\%, and 85.8\% IoU, significantly outperforming existing
approaches. Experiments demonstrate that our explicit dual-selection method
effectively combines the spatial precision of point prompts with the semantic
richness of text prompts, particularly excelling in scenarios involving
semantically complex objects, multiple similar objects, and partial occlusions.
BiPrompt-SAM not only provides a simple yet effective implementation but also
offers a new perspective on multi-modal prompt fusion.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:38:55 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Xu",
"Suzhe",
""
],
[
"Peng",
"Jialin",
""
],
[
"Zhang",
"Chengyuan",
""
]
] | TITLE: BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection
between Point and Text Prompts
ABSTRACT: Segmentation is a fundamental task in computer vision, with prompt-driven
methods gaining prominence due to their flexibility. The recent Segment
Anything Model (SAM) has demonstrated powerful point-prompt segmentation
capabilities, while text-based segmentation models offer rich semantic
understanding. However, existing approaches rarely explore how to effectively
combine these complementary modalities for optimal segmentation performance.
This paper presents BiPrompt-SAM, a novel dual-modal prompt segmentation
framework that fuses the advantages of point and text prompts through an
explicit selection mechanism. Specifically, we leverage SAM's inherent ability
to generate multiple mask candidates, combined with a semantic guidance mask
from text prompts, and explicitly select the most suitable candidate based on
similarity metrics. This approach can be viewed as a simplified Mixture of
Experts (MoE) system, where the point and text modules act as distinct
"experts," and the similarity scoring serves as a rudimentary "gating network."
We conducted extensive evaluations on both the Endovis17 medical dataset and
RefCOCO series natural image datasets. On Endovis17, BiPrompt-SAM achieved
89.55\% mDice and 81.46\% mIoU, comparable to state-of-the-art specialized
medical segmentation models. On the RefCOCO series datasets, our method
attained 87.1\%, 86.5\%, and 85.8\% IoU, significantly outperforming existing
approaches. Experiments demonstrate that our explicit dual-selection method
effectively combines the spatial precision of point prompts with the semantic
richness of text prompts, particularly excelling in scenarios involving
semantically complex objects, multiple similar objects, and partial occlusions.
BiPrompt-SAM not only provides a simple yet effective implementation but also
offers a new perspective on multi-modal prompt fusion.
| no_new_dataset | 0.954351 |
2503.19777 | Vladan Stojni\'c | Vladan Stojni\'c, Yannis Kalantidis, Ji\v{r}\'i Matas, Giorgos Tolias | LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary
Semantic Segmentation | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a training-free method for open-vocabulary semantic segmentation
using Vision-and-Language Models (VLMs). Our approach enhances the initial
per-patch predictions of VLMs through label propagation, which jointly
optimizes predictions by incorporating patch-to-patch relationships. Since VLMs
are primarily optimized for cross-modal alignment and not for intra-modal
similarity, we use a Vision Model (VM) that is observed to better capture these
relationships. We address resolution limitations inherent to patch-based
encoders by applying label propagation at the pixel level as a refinement step,
significantly improving segmentation accuracy near class boundaries. Our
method, called LPOSS+, performs inference over the entire image, avoiding
window-based processing and thereby capturing contextual interactions across
the full image. LPOSS+ achieves state-of-the-art performance among
training-free methods, across a diverse set of datasets. Code:
https://github.com/vladan-stojnic/LPOSS
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:47:13 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Stojnić",
"Vladan",
""
],
[
"Kalantidis",
"Yannis",
""
],
[
"Matas",
"Jiří",
""
],
[
"Tolias",
"Giorgos",
""
]
] | TITLE: LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary
Semantic Segmentation
ABSTRACT: We propose a training-free method for open-vocabulary semantic segmentation
using Vision-and-Language Models (VLMs). Our approach enhances the initial
per-patch predictions of VLMs through label propagation, which jointly
optimizes predictions by incorporating patch-to-patch relationships. Since VLMs
are primarily optimized for cross-modal alignment and not for intra-modal
similarity, we use a Vision Model (VM) that is observed to better capture these
relationships. We address resolution limitations inherent to patch-based
encoders by applying label propagation at the pixel level as a refinement step,
significantly improving segmentation accuracy near class boundaries. Our
method, called LPOSS+, performs inference over the entire image, avoiding
window-based processing and thereby capturing contextual interactions across
the full image. LPOSS+ achieves state-of-the-art performance among
training-free methods, across a diverse set of datasets. Code:
https://github.com/vladan-stojnic/LPOSS
| no_new_dataset | 0.95222 |
2503.19783 | Kartik Thakral | Kartik Thakral, Tamar Glaser, Tal Hassner, Mayank Vatsa, Richa Singh | Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models | Published in CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Existing unlearning algorithms in text-to-image generative models often fail
to preserve the knowledge of semantically related concepts when removing
specific target concepts: a challenge known as adjacency. To address this, we
propose FADE (Fine grained Attenuation for Diffusion Erasure), introducing
adjacency aware unlearning in diffusion models. FADE comprises two components:
(1) the Concept Neighborhood, which identifies an adjacency set of related
concepts, and (2) Mesh Modules, employing a structured combination of
Expungement, Adjacency, and Guidance loss components. These enable precise
erasure of target concepts while preserving fidelity across related and
unrelated concepts. Evaluated on datasets like Stanford Dogs, Oxford Flowers,
CUB, I2P, Imagenette, and ImageNet1k, FADE effectively removes target concepts
with minimal impact on correlated concepts, achieving atleast a 12% improvement
in retention performance over state-of-the-art methods.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:49:48 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Thakral",
"Kartik",
""
],
[
"Glaser",
"Tamar",
""
],
[
"Hassner",
"Tal",
""
],
[
"Vatsa",
"Mayank",
""
],
[
"Singh",
"Richa",
""
]
] | TITLE: Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models
ABSTRACT: Existing unlearning algorithms in text-to-image generative models often fail
to preserve the knowledge of semantically related concepts when removing
specific target concepts: a challenge known as adjacency. To address this, we
propose FADE (Fine grained Attenuation for Diffusion Erasure), introducing
adjacency aware unlearning in diffusion models. FADE comprises two components:
(1) the Concept Neighborhood, which identifies an adjacency set of related
concepts, and (2) Mesh Modules, employing a structured combination of
Expungement, Adjacency, and Guidance loss components. These enable precise
erasure of target concepts while preserving fidelity across related and
unrelated concepts. Evaluated on datasets like Stanford Dogs, Oxford Flowers,
CUB, I2P, Imagenette, and ImageNet1k, FADE effectively removes target concepts
with minimal impact on correlated concepts, achieving atleast a 12% improvement
in retention performance over state-of-the-art methods.
| no_new_dataset | 0.950227 |
2503.19801 | Dong Yang | Zhiyang Liu, Dong Yang, Minghao Zhang, Hanyu Sun, Hong Wu, Huiying
Wang, Wen Shen, Chao Chai, Shuang Xia | SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for
Multi-modal Head MRI | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite that deep learning (DL) methods have presented tremendous potential
in many medical image analysis tasks, the practical applications of medical DL
models are limited due to the lack of enough data samples with manual
annotations. By noting that the clinical radiology examinations are associated
with radiology reports that describe the images, we propose to develop a
foundation model for multi-model head MRI by using contrastive learning on the
images and the corresponding radiology findings. In particular, a contrastive
learning framework is proposed, where a mixed syntax and semantic similarity
matching metric is integrated to reduce the thirst of extreme large dataset in
conventional contrastive learning framework. Our proposed similarity enhanced
contrastive language image pretraining (SeLIP) is able to effectively extract
more useful features. Experiments revealed that our proposed SeLIP performs
well in many downstream tasks including image-text retrieval task,
classification task, and image segmentation, which highlights the importance of
considering the similarities among texts describing different images in
developing medical image foundation models.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 16:09:45 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Liu",
"Zhiyang",
""
],
[
"Yang",
"Dong",
""
],
[
"Zhang",
"Minghao",
""
],
[
"Sun",
"Hanyu",
""
],
[
"Wu",
"Hong",
""
],
[
"Wang",
"Huiying",
""
],
[
"Shen",
"Wen",
""
],
[
"Chai",
"Chao",
""
],
[
"Xia",
"Shuang",
""
]
] | TITLE: SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for
Multi-modal Head MRI
ABSTRACT: Despite that deep learning (DL) methods have presented tremendous potential
in many medical image analysis tasks, the practical applications of medical DL
models are limited due to the lack of enough data samples with manual
annotations. By noting that the clinical radiology examinations are associated
with radiology reports that describe the images, we propose to develop a
foundation model for multi-model head MRI by using contrastive learning on the
images and the corresponding radiology findings. In particular, a contrastive
learning framework is proposed, where a mixed syntax and semantic similarity
matching metric is integrated to reduce the thirst of extreme large dataset in
conventional contrastive learning framework. Our proposed similarity enhanced
contrastive language image pretraining (SeLIP) is able to effectively extract
more useful features. Experiments revealed that our proposed SeLIP performs
well in many downstream tasks including image-text retrieval task,
classification task, and image segmentation, which highlights the importance of
considering the similarities among texts describing different images in
developing medical image foundation models.
| no_new_dataset | 0.949153 |
2503.19802 | Laura Kurek | Laura Kurek, Kevin Zheng, Eric Gilbert, Ceren Budak | Outsourcing an Information Operation: A Complete Dataset of Tenet
Media's Podcasts on Rumble | null | null | null | null | cs.SI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Tenet Media, a U.S.-based, right-wing media company, hired six established
podcasters to create content related to U.S. politics and culture during the
2024 U.S. presidential election cycle. After publishing content on YouTube and
Rumble for nearly a year, Tenet Media was declared by the U.S. government to be
funded entirely by Russia -- making it effectively an outsourced
state-sponsored information operation (SSIO). We present a complete dataset of
the 560 podcast videos published by the Tenet Media channel on the
video-sharing platform Rumble between November 2023 and September 2024. Our
dataset includes video metadata and user comments, as well as high-quality
video transcriptions, representing over 300 hours of video content. This
dataset provides researchers with material to study a Russian SSIO, and notably
on Rumble, which is an understudied platform in SSIO scholarship.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 16:11:51 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Kurek",
"Laura",
""
],
[
"Zheng",
"Kevin",
""
],
[
"Gilbert",
"Eric",
""
],
[
"Budak",
"Ceren",
""
]
] | TITLE: Outsourcing an Information Operation: A Complete Dataset of Tenet
Media's Podcasts on Rumble
ABSTRACT: Tenet Media, a U.S.-based, right-wing media company, hired six established
podcasters to create content related to U.S. politics and culture during the
2024 U.S. presidential election cycle. After publishing content on YouTube and
Rumble for nearly a year, Tenet Media was declared by the U.S. government to be
funded entirely by Russia -- making it effectively an outsourced
state-sponsored information operation (SSIO). We present a complete dataset of
the 560 podcast videos published by the Tenet Media channel on the
video-sharing platform Rumble between November 2023 and September 2024. Our
dataset includes video metadata and user comments, as well as high-quality
video transcriptions, representing over 300 hours of video content. This
dataset provides researchers with material to study a Russian SSIO, and notably
on Rumble, which is an understudied platform in SSIO scholarship.
| new_dataset | 0.963231 |
2503.19804 | Manjushree Aithal | Manjushree Aithal, Rosaura G. VidalMata, Manikandtan Kartha, Gong
Chen, Eashan Adhikarla, Lucas N. Kirsten, Zhicheng Fu, Nikhil A.
Madhusudhana, and Joe Nasti | LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset | Dataset will be released upon publication | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Low-light image enhancement is crucial for a myriad of applications, from
night vision and surveillance, to autonomous driving. However, due to the
inherent limitations that come in hand with capturing images in
low-illumination environments, the task of enhancing such scenes still presents
a formidable challenge. To advance research in this field, we introduce our Low
Exposure Night Vision (LENVIZ) Dataset, a comprehensive multi-exposure
benchmark dataset for low-light image enhancement comprising of over 230K
frames showcasing 24K real-world indoor and outdoor, with-and without human,
scenes. Captured using 3 different camera sensors, LENVIZ offers a wide range
of lighting conditions, noise levels, and scene complexities, making it the
largest publicly available up-to 4K resolution benchmark in the field. LENVIZ
includes high quality human-generated ground truth, for which each
multi-exposure low-light scene has been meticulously curated and edited by
expert photographers to ensure optimal image quality. Furthermore, we also
conduct a comprehensive analysis of current state-of-the-art low-light image
enhancement techniques on our dataset and highlight potential areas of
improvement.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 16:12:28 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Aithal",
"Manjushree",
""
],
[
"VidalMata",
"Rosaura G.",
""
],
[
"Kartha",
"Manikandtan",
""
],
[
"Chen",
"Gong",
""
],
[
"Adhikarla",
"Eashan",
""
],
[
"Kirsten",
"Lucas N.",
""
],
[
"Fu",
"Zhicheng",
""
],
[
"Madhusudhana",
"Nikhil A.",
""
],
[
"Nasti",
"Joe",
""
]
] | TITLE: LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset
ABSTRACT: Low-light image enhancement is crucial for a myriad of applications, from
night vision and surveillance, to autonomous driving. However, due to the
inherent limitations that come in hand with capturing images in
low-illumination environments, the task of enhancing such scenes still presents
a formidable challenge. To advance research in this field, we introduce our Low
Exposure Night Vision (LENVIZ) Dataset, a comprehensive multi-exposure
benchmark dataset for low-light image enhancement comprising of over 230K
frames showcasing 24K real-world indoor and outdoor, with-and without human,
scenes. Captured using 3 different camera sensors, LENVIZ offers a wide range
of lighting conditions, noise levels, and scene complexities, making it the
largest publicly available up-to 4K resolution benchmark in the field. LENVIZ
includes high quality human-generated ground truth, for which each
multi-exposure low-light scene has been meticulously curated and edited by
expert photographers to ensure optimal image quality. Furthermore, we also
conduct a comprehensive analysis of current state-of-the-art low-light image
enhancement techniques on our dataset and highlight potential areas of
improvement.
| new_dataset | 0.961061 |
2503.19814 | Reinhard Maurer | Lukas H\"ormann, Wojciech G. Stark, Reinhard J. Maurer | Machine Learning and Data-Driven Methods in Computational Surface and
Interface Science | 27 pages, 5 figures | null | null | null | cond-mat.mtrl-sci physics.comp-ph | http://creativecommons.org/licenses/by/4.0/ | Nanoscale design of surfaces and interfaces is essential for modern
technologies like organic LEDs, batteries, fuel cells, superlubricating
surfaces, and heterogeneous catalysis. However, these systems often exhibit
complex surface reconstructions and polymorphism, with properties influenced by
kinetic processes and dynamic behavior. A lack of accurate and scalable
simulation tools has limited computational modeling of surfaces and interfaces.
Recently, machine learning and data-driven methods have expanded the
capabilities of theoretical modeling, enabling, for example, the routine use of
machine-learned interatomic potentials to predict energies and forces across
numerous structures. Despite these advances, significant challenges remain,
including the scarcity of large, consistent datasets and the need for
computational and data-efficient machine learning methods. Additionally, a
major challenge lies in the lack of accurate reference data and electronic
structure methods for interfaces. Density Functional Theory, while effective
for bulk materials, is less reliable for surfaces, and too few accurate
experimental studies on interface structure and stability exist. Here, we will
sketch the current state of data-driven methods and machine learning in
computational surface science and provide a perspective on how these methods
will shape the field in the future.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 16:26:28 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Hörmann",
"Lukas",
""
],
[
"Stark",
"Wojciech G.",
""
],
[
"Maurer",
"Reinhard J.",
""
]
] | TITLE: Machine Learning and Data-Driven Methods in Computational Surface and
Interface Science
ABSTRACT: Nanoscale design of surfaces and interfaces is essential for modern
technologies like organic LEDs, batteries, fuel cells, superlubricating
surfaces, and heterogeneous catalysis. However, these systems often exhibit
complex surface reconstructions and polymorphism, with properties influenced by
kinetic processes and dynamic behavior. A lack of accurate and scalable
simulation tools has limited computational modeling of surfaces and interfaces.
Recently, machine learning and data-driven methods have expanded the
capabilities of theoretical modeling, enabling, for example, the routine use of
machine-learned interatomic potentials to predict energies and forces across
numerous structures. Despite these advances, significant challenges remain,
including the scarcity of large, consistent datasets and the need for
computational and data-efficient machine learning methods. Additionally, a
major challenge lies in the lack of accurate reference data and electronic
structure methods for interfaces. Density Functional Theory, while effective
for bulk materials, is less reliable for surfaces, and too few accurate
experimental studies on interface structure and stability exist. Here, we will
sketch the current state of data-driven methods and machine learning in
computational surface science and provide a perspective on how these methods
will shape the field in the future.
| no_new_dataset | 0.940735 |
2503.19819 | Pratibha Kumari | Pratibha Kumari, Afshin Bozorgpour, Daniel Reisenb\"uchler, Edgar
Jost, Martina Crysandt, Christian Matek, Dorit Merhof | Domain-incremental White Blood Cell Classification with Privacy-aware
Continual Learning | null | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | White blood cell (WBC) classification plays a vital role in hematology for
diagnosing various medical conditions. However, it faces significant challenges
due to domain shifts caused by variations in sample sources (e.g., blood or
bone marrow) and differing imaging conditions across hospitals. Traditional
deep learning models often suffer from catastrophic forgetting in such dynamic
environments, while foundation models, though generally robust, experience
performance degradation when the distribution of inference data differs from
that of the training data. To address these challenges, we propose a generative
replay-based Continual Learning (CL) strategy designed to prevent forgetting in
foundation models for WBC classification. Our method employs lightweight
generators to mimic past data with a synthetic latent representation to enable
privacy-preserving replay. To showcase the effectiveness, we carry out
extensive experiments with a total of four datasets with different task
ordering and four backbone models including ResNet50, RetCCL, CTransPath, and
UNI. Experimental results demonstrate that conventional fine-tuning methods
degrade performance on previously learned tasks and struggle with domain
shifts. In contrast, our continual learning strategy effectively mitigates
catastrophic forgetting, preserving model performance across varying domains.
This work presents a practical solution for maintaining reliable WBC
classification in real-world clinical settings, where data distributions
frequently evolve.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 16:30:58 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Kumari",
"Pratibha",
""
],
[
"Bozorgpour",
"Afshin",
""
],
[
"Reisenbüchler",
"Daniel",
""
],
[
"Jost",
"Edgar",
""
],
[
"Crysandt",
"Martina",
""
],
[
"Matek",
"Christian",
""
],
[
"Merhof",
"Dorit",
""
]
] | TITLE: Domain-incremental White Blood Cell Classification with Privacy-aware
Continual Learning
ABSTRACT: White blood cell (WBC) classification plays a vital role in hematology for
diagnosing various medical conditions. However, it faces significant challenges
due to domain shifts caused by variations in sample sources (e.g., blood or
bone marrow) and differing imaging conditions across hospitals. Traditional
deep learning models often suffer from catastrophic forgetting in such dynamic
environments, while foundation models, though generally robust, experience
performance degradation when the distribution of inference data differs from
that of the training data. To address these challenges, we propose a generative
replay-based Continual Learning (CL) strategy designed to prevent forgetting in
foundation models for WBC classification. Our method employs lightweight
generators to mimic past data with a synthetic latent representation to enable
privacy-preserving replay. To showcase the effectiveness, we carry out
extensive experiments with a total of four datasets with different task
ordering and four backbone models including ResNet50, RetCCL, CTransPath, and
UNI. Experimental results demonstrate that conventional fine-tuning methods
degrade performance on previously learned tasks and struggle with domain
shifts. In contrast, our continual learning strategy effectively mitigates
catastrophic forgetting, preserving model performance across varying domains.
This work presents a practical solution for maintaining reliable WBC
classification in real-world clinical settings, where data distributions
frequently evolve.
| no_new_dataset | 0.941868 |
2503.19844 | Spencer Stewart | Zhao Fang, Liang-Chun Wu, Xuening Kong, Spencer Dean Stewart | A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and
Named Entity Recognition for Historical Chinese Sources, 1900-1950 | Accepted to NLP4DH 2025 at NAACL 2025 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper compares large language models (LLMs) and traditional natural
language processing (NLP) tools for performing word segmentation,
part-of-speech (POS) tagging, and named entity recognition (NER) on Chinese
texts from 1900 to 1950. Historical Chinese documents pose challenges for text
analysis due to their logographic script, the absence of natural word
boundaries, and significant linguistic changes. Using a sample dataset from the
Shanghai Library Republican Journal corpus, traditional tools such as Jieba and
spaCy are compared to LLMs, including GPT-4o, Claude 3.5, and the GLM series.
The results show that LLMs outperform traditional methods in all metrics,
albeit at considerably higher computational costs, highlighting a trade-off
between accuracy and efficiency. Additionally, LLMs better handle
genre-specific challenges such as poetry and temporal variations (i.e.,
pre-1920 versus post-1920 texts), demonstrating that their contextual learning
capabilities can advance NLP approaches to historical texts by reducing the
need for domain-specific training data.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 17:07:21 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Fang",
"Zhao",
""
],
[
"Wu",
"Liang-Chun",
""
],
[
"Kong",
"Xuening",
""
],
[
"Stewart",
"Spencer Dean",
""
]
] | TITLE: A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and
Named Entity Recognition for Historical Chinese Sources, 1900-1950
ABSTRACT: This paper compares large language models (LLMs) and traditional natural
language processing (NLP) tools for performing word segmentation,
part-of-speech (POS) tagging, and named entity recognition (NER) on Chinese
texts from 1900 to 1950. Historical Chinese documents pose challenges for text
analysis due to their logographic script, the absence of natural word
boundaries, and significant linguistic changes. Using a sample dataset from the
Shanghai Library Republican Journal corpus, traditional tools such as Jieba and
spaCy are compared to LLMs, including GPT-4o, Claude 3.5, and the GLM series.
The results show that LLMs outperform traditional methods in all metrics,
albeit at considerably higher computational costs, highlighting a trade-off
between accuracy and efficiency. Additionally, LLMs better handle
genre-specific challenges such as poetry and temporal variations (i.e.,
pre-1920 versus post-1920 texts), demonstrating that their contextual learning
capabilities can advance NLP approaches to historical texts by reducing the
need for domain-specific training data.
| no_new_dataset | 0.948489 |
2503.19851 | Xinpeng Li | Xinpeng Li, Shijian Deng, Bolin Lai, Weiguo Pian, James M. Rehg,
Yapeng Tian | Towards Online Multi-Modal Social Interaction Understanding | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Multimodal social interaction understanding (MMSI) is critical in human-robot
interaction systems. In real-world scenarios, AI agents are required to provide
real-time feedback. However, existing models often depend on both past and
future contexts, which hinders them from applying to real-world problems. To
bridge this gap, we propose an online MMSI setting, where the model must
resolve MMSI tasks using only historical information, such as recorded
dialogues and video streams. To address the challenges of missing the useful
future context, we develop a novel framework, named Online-MMSI-VLM, that
leverages two complementary strategies: multi-party conversation forecasting
and social-aware visual prompting with multi-modal large language models.
First, to enrich linguistic context, the multi-party conversation forecasting
simulates potential future utterances in a coarse-to-fine manner, anticipating
upcoming speaker turns and then generating fine-grained conversational details.
Second, to effectively incorporate visual social cues like gaze and gesture,
social-aware visual prompting highlights the social dynamics in video with
bounding boxes and body keypoints for each person and frame. Extensive
experiments on three tasks and two datasets demonstrate that our method
achieves state-of-the-art performance and significantly outperforms baseline
models, indicating its effectiveness on Online-MMSI. The code and pre-trained
models will be publicly released at: https://github.com/Sampson-Lee/OnlineMMSI.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 17:17:19 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Li",
"Xinpeng",
""
],
[
"Deng",
"Shijian",
""
],
[
"Lai",
"Bolin",
""
],
[
"Pian",
"Weiguo",
""
],
[
"Rehg",
"James M.",
""
],
[
"Tian",
"Yapeng",
""
]
] | TITLE: Towards Online Multi-Modal Social Interaction Understanding
ABSTRACT: Multimodal social interaction understanding (MMSI) is critical in human-robot
interaction systems. In real-world scenarios, AI agents are required to provide
real-time feedback. However, existing models often depend on both past and
future contexts, which hinders them from applying to real-world problems. To
bridge this gap, we propose an online MMSI setting, where the model must
resolve MMSI tasks using only historical information, such as recorded
dialogues and video streams. To address the challenges of missing the useful
future context, we develop a novel framework, named Online-MMSI-VLM, that
leverages two complementary strategies: multi-party conversation forecasting
and social-aware visual prompting with multi-modal large language models.
First, to enrich linguistic context, the multi-party conversation forecasting
simulates potential future utterances in a coarse-to-fine manner, anticipating
upcoming speaker turns and then generating fine-grained conversational details.
Second, to effectively incorporate visual social cues like gaze and gesture,
social-aware visual prompting highlights the social dynamics in video with
bounding boxes and body keypoints for each person and frame. Extensive
experiments on three tasks and two datasets demonstrate that our method
achieves state-of-the-art performance and significantly outperforms baseline
models, indicating its effectiveness on Online-MMSI. The code and pre-trained
models will be publicly released at: https://github.com/Sampson-Lee/OnlineMMSI.
| no_new_dataset | 0.940353 |
2503.19855 | Yunjie Ji | Xiaoyu Tian, Sitong Zhao, Haotian Wang, Shuaiting Chen, Yunjie Ji,
Yiping Peng, Han Zhao, Xiangang Li | Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time
Thinking | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in large language models (LLMs), such as OpenAI-o1 and
DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where
extended reasoning processes substantially enhance model performance. Despite
this, current models are constrained by limitations in handling long texts and
reinforcement learning (RL) training efficiency. To address these issues, we
propose a simple yet effective test-time scaling approach Multi-round Thinking.
This method iteratively refines model reasoning by leveraging previous answers
as prompts for subsequent rounds. Extensive experiments across multiple models,
including QwQ-32B and DeepSeek-R1, consistently show performance improvements
on various benchmarks such as AIME 2024, MATH-500, GPQA-diamond, and
LiveCodeBench. For instance, the accuracy of QwQ-32B improved from 80.3% (Round
1) to 82.1% (Round 2) on the AIME 2024 dataset, while DeepSeek-R1 showed a
similar increase from 79.7% to 82.0%. These results confirm that Multi-round
Thinking is a broadly applicable, straightforward approach to achieving stable
enhancements in model performance, underscoring its potential for future
developments in test-time scaling techniques. The key prompt: {Original
question prompt} The assistant's previous answer is: <answer> {last round
answer} </answer>, and please re-answer.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 17:19:38 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Tian",
"Xiaoyu",
""
],
[
"Zhao",
"Sitong",
""
],
[
"Wang",
"Haotian",
""
],
[
"Chen",
"Shuaiting",
""
],
[
"Ji",
"Yunjie",
""
],
[
"Peng",
"Yiping",
""
],
[
"Zhao",
"Han",
""
],
[
"Li",
"Xiangang",
""
]
] | TITLE: Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time
Thinking
ABSTRACT: Recent advances in large language models (LLMs), such as OpenAI-o1 and
DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where
extended reasoning processes substantially enhance model performance. Despite
this, current models are constrained by limitations in handling long texts and
reinforcement learning (RL) training efficiency. To address these issues, we
propose a simple yet effective test-time scaling approach Multi-round Thinking.
This method iteratively refines model reasoning by leveraging previous answers
as prompts for subsequent rounds. Extensive experiments across multiple models,
including QwQ-32B and DeepSeek-R1, consistently show performance improvements
on various benchmarks such as AIME 2024, MATH-500, GPQA-diamond, and
LiveCodeBench. For instance, the accuracy of QwQ-32B improved from 80.3% (Round
1) to 82.1% (Round 2) on the AIME 2024 dataset, while DeepSeek-R1 showed a
similar increase from 79.7% to 82.0%. These results confirm that Multi-round
Thinking is a broadly applicable, straightforward approach to achieving stable
enhancements in model performance, underscoring its potential for future
developments in test-time scaling techniques. The key prompt: {Original
question prompt} The assistant's previous answer is: <answer> {last round
answer} </answer>, and please re-answer.
| no_new_dataset | 0.940844 |
2503.19874 | Youguang Chen | Youguang Chen and George Biros | Extensions of regret-minimization algorithm for optimal design | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore extensions and applications of the regret minimization framework
introduced by~\cite{design} for solving optimal experimental design problems.
Specifically, we incorporate the entropy regularizer into this framework,
leading to a novel sample selection objective and a provable sample complexity
bound that guarantees a $(1+\epsilon)$-near optimal solution. We further extend
the method to handle regularized optimal design settings. As an application, we
use our algorithm to select a small set of representative samples from image
classification datasets without relying on label information. To evaluate the
quality of the selected samples, we train a logistic regression model and
compare performance against several baseline sampling strategies. Experimental
results on MNIST, CIFAR-10, and a 50-class subset of ImageNet show that our
approach consistently outperforms competing methods in most cases.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 17:37:09 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Chen",
"Youguang",
""
],
[
"Biros",
"George",
""
]
] | TITLE: Extensions of regret-minimization algorithm for optimal design
ABSTRACT: We explore extensions and applications of the regret minimization framework
introduced by~\cite{design} for solving optimal experimental design problems.
Specifically, we incorporate the entropy regularizer into this framework,
leading to a novel sample selection objective and a provable sample complexity
bound that guarantees a $(1+\epsilon)$-near optimal solution. We further extend
the method to handle regularized optimal design settings. As an application, we
use our algorithm to select a small set of representative samples from image
classification datasets without relying on label information. To evaluate the
quality of the selected samples, we train a logistic regression model and
compare performance against several baseline sampling strategies. Experimental
results on MNIST, CIFAR-10, and a 50-class subset of ImageNet show that our
approach consistently outperforms competing methods in most cases.
| no_new_dataset | 0.947137 |
2503.19886 | Abdulmoneam Ali | Abdulmoneam Ali and Ahmed Arafa | RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized
Federated Learning | to appear in the 2025 IEEE International Conference on Communications | null | null | null | cs.LG cs.DC cs.IT cs.NI eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of cluster identity estimation in a personalized
federated learning (PFL) setting in which users aim to learn different personal
models. The backbone of effective learning in such a setting is to cluster
users into groups whose objectives are similar. A typical approach in the
literature is to achieve this by training users' data on different proposed
personal models and assign them to groups based on which model achieves the
lowest value of the users' loss functions. This process is to be done
iteratively until group identities converge. A key challenge in such a setting
arises when users have noisy labeled data, which may produce misleading values
of their loss functions, and hence lead to ineffective clustering. To overcome
this challenge, we propose a label-agnostic data similarity-based clustering
algorithm, coined RCC-PFL, with three main advantages: the cluster identity
estimation procedure is independent from the training labels; it is a one-shot
clustering algorithm performed prior to the training; and it requires fewer
communication rounds and less computation compared to iterative-based
clustering methods. We validate our proposed algorithm using various models and
datasets and show that it outperforms multiple baselines in terms of average
accuracy and variance reduction.
| [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 17:50:54 GMT"
}
] | 2025-03-26T00:00:00 | [
[
"Ali",
"Abdulmoneam",
""
],
[
"Arafa",
"Ahmed",
""
]
] | TITLE: RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized
Federated Learning
ABSTRACT: We address the problem of cluster identity estimation in a personalized
federated learning (PFL) setting in which users aim to learn different personal
models. The backbone of effective learning in such a setting is to cluster
users into groups whose objectives are similar. A typical approach in the
literature is to achieve this by training users' data on different proposed
personal models and assign them to groups based on which model achieves the
lowest value of the users' loss functions. This process is to be done
iteratively until group identities converge. A key challenge in such a setting
arises when users have noisy labeled data, which may produce misleading values
of their loss functions, and hence lead to ineffective clustering. To overcome
this challenge, we propose a label-agnostic data similarity-based clustering
algorithm, coined RCC-PFL, with three main advantages: the cluster identity
estimation procedure is independent from the training labels; it is a one-shot
clustering algorithm performed prior to the training; and it requires fewer
communication rounds and less computation compared to iterative-based
clustering methods. We validate our proposed algorithm using various models and
datasets and show that it outperforms multiple baselines in terms of average
accuracy and variance reduction.
| no_new_dataset | 0.944842 |
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