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2503.17402
Badih Ghattas
Oscar L. Cruz-Gonz\'alez, Val\'erie Deplano, Badih Ghattas
Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks
null
null
null
null
cs.LG stat.CO stat.ML
http://creativecommons.org/publicdomain/zero/1.0/
Due to the limited accuracy of 4D Magnetic Resonance Imaging (MRI) in identifying hemodynamics in cardiovascular diseases, the challenges in obtaining patient-specific flow boundary conditions, and the computationally demanding and time-consuming nature of Computational Fluid Dynamics (CFD) simulations, it is crucial to explore new data assimilation algorithms that offer possible alternatives to these limitations. In the present work, we study Physics-Informed Neural Networks (PINNs), Deep Operator Networks (DeepONets), and their Physics-Informed extensions (PI-DeepONets) in predicting vascular flow simulations in the context of a 3D Abdominal Aortic Aneurysm (AAA) idealized model. PINN is a technique that combines deep neural networks with the fundamental principles of physics, incorporating the physics laws, which are given as partial differential equations, directly into loss functions used during the training process. On the other hand, DeepONet is designed to learn nonlinear operators from data and is particularly useful in studying parametric partial differential equations (PDEs), e.g., families of PDEs with different source terms, boundary conditions, or initial conditions. Here, we adapt the approaches to address the particular use case of AAA by integrating the 3D Navier-Stokes equations (NSE) as the physical laws governing fluid dynamics. In addition, we follow best practices to enhance the capabilities of the models by effectively capturing the underlying physics of the problem under study. The advantages and limitations of each approach are highlighted through a series of relevant application cases. We validate our results by comparing them with CFD simulations for benchmark datasets, demonstrating good agreements and emphasizing those cases where improvements in computational efficiency are observed.
[ { "version": "v1", "created": "Wed, 19 Mar 2025 22:38:52 GMT" } ]
2025-03-25T00:00:00
[ [ "Cruz-González", "Oscar L.", "" ], [ "Deplano", "Valérie", "" ], [ "Ghattas", "Badih", "" ] ]
TITLE: Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks ABSTRACT: Due to the limited accuracy of 4D Magnetic Resonance Imaging (MRI) in identifying hemodynamics in cardiovascular diseases, the challenges in obtaining patient-specific flow boundary conditions, and the computationally demanding and time-consuming nature of Computational Fluid Dynamics (CFD) simulations, it is crucial to explore new data assimilation algorithms that offer possible alternatives to these limitations. In the present work, we study Physics-Informed Neural Networks (PINNs), Deep Operator Networks (DeepONets), and their Physics-Informed extensions (PI-DeepONets) in predicting vascular flow simulations in the context of a 3D Abdominal Aortic Aneurysm (AAA) idealized model. PINN is a technique that combines deep neural networks with the fundamental principles of physics, incorporating the physics laws, which are given as partial differential equations, directly into loss functions used during the training process. On the other hand, DeepONet is designed to learn nonlinear operators from data and is particularly useful in studying parametric partial differential equations (PDEs), e.g., families of PDEs with different source terms, boundary conditions, or initial conditions. Here, we adapt the approaches to address the particular use case of AAA by integrating the 3D Navier-Stokes equations (NSE) as the physical laws governing fluid dynamics. In addition, we follow best practices to enhance the capabilities of the models by effectively capturing the underlying physics of the problem under study. The advantages and limitations of each approach are highlighted through a series of relevant application cases. We validate our results by comparing them with CFD simulations for benchmark datasets, demonstrating good agreements and emphasizing those cases where improvements in computational efficiency are observed.
2503.17403
Azim Akhtarshenas
Azim Akhtarshenas, Afshin Dini, Navid Ayoobi
ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities. We examine its integration into various domains across industries such as customer service, education, healthcare, and entertainment. A comparative analysis with other LLMs highlights ChatGPT's unique features and performance metrics. Regarding benchmarks, the paper examines ChatGPT's comparative performance against other LLMs and discusses potential risks such as misinformation, bias, and data privacy concerns. Additionally, we offer a number of figures and tables that outline the backdrop of the discussion, the main ideas of the article, the numerous LLM models, a thorough list of datasets used for pre-training, fine-tuning, and evaluation, as well as particular LLM applications with pertinent references. Finally, we identify future research directions and technological advancements, underscoring the evolving landscape of LLMs and their profound impact on artificial intelligence Artificial Intelligence (AI) and society.
[ { "version": "v1", "created": "Wed, 19 Mar 2025 22:55:08 GMT" } ]
2025-03-25T00:00:00
[ [ "Akhtarshenas", "Azim", "" ], [ "Dini", "Afshin", "" ], [ "Ayoobi", "Navid", "" ] ]
TITLE: ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models ABSTRACT: Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities. We examine its integration into various domains across industries such as customer service, education, healthcare, and entertainment. A comparative analysis with other LLMs highlights ChatGPT's unique features and performance metrics. Regarding benchmarks, the paper examines ChatGPT's comparative performance against other LLMs and discusses potential risks such as misinformation, bias, and data privacy concerns. Additionally, we offer a number of figures and tables that outline the backdrop of the discussion, the main ideas of the article, the numerous LLM models, a thorough list of datasets used for pre-training, fine-tuning, and evaluation, as well as particular LLM applications with pertinent references. Finally, we identify future research directions and technological advancements, underscoring the evolving landscape of LLMs and their profound impact on artificial intelligence Artificial Intelligence (AI) and society.
2503.17406
Haochen Zhang
Haochen Zhang, Nader Zantout, Pujith Kachana, Ji Zhang, Wenshan Wang
IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D Scenes
Accepted to ICRA 2025. Code available at https://github.com/HaochenZ11/IRef-VLA. arXiv admin note: text overlap with arXiv:2411.03540
null
null
null
cs.CV cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With the recent rise of large language models, vision-language models, and other general foundation models, there is growing potential for multimodal, multi-task robotics that can operate in diverse environments given natural language input. One such application is indoor navigation using natural language instructions. However, despite recent progress, this problem remains challenging due to the 3D spatial reasoning and semantic understanding required. Additionally, the language used may be imperfect or misaligned with the scene, further complicating the task. To address this challenge, we curate a benchmark dataset, IRef-VLA, for Interactive Referential Vision and Language-guided Action in 3D Scenes with imperfect references. IRef-VLA is the largest real-world dataset for the referential grounding task, consisting of over 11.5K scanned 3D rooms from existing datasets, 7.6M heuristically generated semantic relations, and 4.7M referential statements. Our dataset also contains semantic object and room annotations, scene graphs, navigable free space annotations, and is augmented with statements where the language has imperfections or ambiguities. We verify the generalizability of our dataset by evaluating with state-of-the-art models to obtain a performance baseline and also develop a graph-search baseline to demonstrate the performance bound and generation of alternatives using scene-graph knowledge. With this benchmark, we aim to provide a resource for 3D scene understanding that aids the development of robust, interactive navigation systems. The dataset and all source code is publicly released at https://github.com/HaochenZ11/IRef-VLA.
[ { "version": "v1", "created": "Thu, 20 Mar 2025 16:16:10 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhang", "Haochen", "" ], [ "Zantout", "Nader", "" ], [ "Kachana", "Pujith", "" ], [ "Zhang", "Ji", "" ], [ "Wang", "Wenshan", "" ] ]
TITLE: IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D Scenes ABSTRACT: With the recent rise of large language models, vision-language models, and other general foundation models, there is growing potential for multimodal, multi-task robotics that can operate in diverse environments given natural language input. One such application is indoor navigation using natural language instructions. However, despite recent progress, this problem remains challenging due to the 3D spatial reasoning and semantic understanding required. Additionally, the language used may be imperfect or misaligned with the scene, further complicating the task. To address this challenge, we curate a benchmark dataset, IRef-VLA, for Interactive Referential Vision and Language-guided Action in 3D Scenes with imperfect references. IRef-VLA is the largest real-world dataset for the referential grounding task, consisting of over 11.5K scanned 3D rooms from existing datasets, 7.6M heuristically generated semantic relations, and 4.7M referential statements. Our dataset also contains semantic object and room annotations, scene graphs, navigable free space annotations, and is augmented with statements where the language has imperfections or ambiguities. We verify the generalizability of our dataset by evaluating with state-of-the-art models to obtain a performance baseline and also develop a graph-search baseline to demonstrate the performance bound and generation of alternatives using scene-graph knowledge. With this benchmark, we aim to provide a resource for 3D scene understanding that aids the development of robust, interactive navigation systems. The dataset and all source code is publicly released at https://github.com/HaochenZ11/IRef-VLA.
2503.17408
Pablo Rivas
Maisha Binte Rashid, Pablo Rivas
Leveraging OpenFlamingo for Multimodal Embedding Analysis of C2C Car Parts Data
The 26th International Conference on Artificial Intelligence (ICAI'24: July 22-25, 2024; Las Vegas, USA)
null
null
null
cs.LG cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we aim to investigate the capabilities of multimodal machine learning models, particularly the OpenFlamingo model, in processing a large-scale dataset of consumer-to-consumer (C2C) online posts related to car parts. We have collected data from two platforms, OfferUp and Craigslist, resulting in a dataset of over 1.2 million posts with their corresponding images. The OpenFlamingo model was used to extract embeddings for the text and image of each post. We used $k$-means clustering on the joint embeddings to identify underlying patterns and commonalities among the posts. We have found that most clusters contain a pattern, but some clusters showed no internal patterns. The results provide insight into the fact that OpenFlamingo can be used for finding patterns in large datasets but needs some modification in the architecture according to the dataset.
[ { "version": "v1", "created": "Thu, 20 Mar 2025 19:35:15 GMT" } ]
2025-03-25T00:00:00
[ [ "Rashid", "Maisha Binte", "" ], [ "Rivas", "Pablo", "" ] ]
TITLE: Leveraging OpenFlamingo for Multimodal Embedding Analysis of C2C Car Parts Data ABSTRACT: In this paper, we aim to investigate the capabilities of multimodal machine learning models, particularly the OpenFlamingo model, in processing a large-scale dataset of consumer-to-consumer (C2C) online posts related to car parts. We have collected data from two platforms, OfferUp and Craigslist, resulting in a dataset of over 1.2 million posts with their corresponding images. The OpenFlamingo model was used to extract embeddings for the text and image of each post. We used $k$-means clustering on the joint embeddings to identify underlying patterns and commonalities among the posts. We have found that most clusters contain a pattern, but some clusters showed no internal patterns. The results provide insight into the fact that OpenFlamingo can be used for finding patterns in large datasets but needs some modification in the architecture according to the dataset.
2503.17410
Josef Koumar
Josef Koumar, Timotej Smole\v{n}, Kamil Je\v{r}\'abek, Tom\'a\v{s} \v{C}ejka
Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
null
null
null
null
cs.LG cs.AI cs.NI
http://creativecommons.org/licenses/by/4.0/
Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivariate time series over 40 weeks. Our findings highlight the balance between prediction accuracy and computational efficiency across different levels of network granularity. Additionally, this work establishes a reproducible methodology that facilitates direct comparison of existing approaches, explores their strengths and weaknesses, and provides a benchmark for future studies using this dataset.
[ { "version": "v1", "created": "Thu, 20 Mar 2025 21:04:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Koumar", "Josef", "" ], [ "Smoleň", "Timotej", "" ], [ "Jeřábek", "Kamil", "" ], [ "Čejka", "Tomáš", "" ] ]
TITLE: Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting ABSTRACT: Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates state-of-the-art deep learning models on CESNET-TimeSeries24, a recently published, comprehensive real-world network traffic dataset from the ISP network CESNET3 spanning multivariate time series over 40 weeks. Our findings highlight the balance between prediction accuracy and computational efficiency across different levels of network granularity. Additionally, this work establishes a reproducible methodology that facilitates direct comparison of existing approaches, explores their strengths and weaknesses, and provides a benchmark for future studies using this dataset.
2503.17416
Corina P\u{a}s\u{a}reanu
Boyue Caroline Hu, Divya Gopinath, Corina S. Pasareanu, Nina Narodytska, Ravi Mangal, Susmit Jha
Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)
CAIN 2025 (4th International Conference on AI Engineering -- Software Engineering for AI)
null
null
null
cs.SE cs.AI cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper, we explore multi-modal Vision-Language Models (VLMs), such as CLIP, to automatically interpret the opaque representation space of vision models using natural language. This in turn, enables a semantic analysis of model behavior using human-understandable concepts, without requiring costly human annotations. Key to our approach is the notion of semantic heatmap, that succinctly captures the statistical properties of DNNs in terms of the concepts discovered with the VLM and that are computed off-line using a held-out data set. We show the utility of semantic heatmaps for fault localization -- an essential step in debugging -- in vision models. Our proposed technique helps localize the fault in the network (encoder vs head) and also highlights the responsible high-level concepts, by leveraging novel differential heatmaps, which summarize the semantic differences between the correct and incorrect behaviour of the analyzed DNN. We further propose a lightweight runtime analysis to detect and filter-out defects at runtime, thus improving the reliability of the analyzed DNNs. The runtime analysis works by measuring and comparing the similarity between the heatmap computed for a new (unseen) input and the heatmaps computed a-priori for correct vs incorrect DNN behavior. We consider two types of defects: misclassifications and vulnerabilities to adversarial attacks. We demonstrate the debugging and runtime analysis on a case study involving a complex ResNet-based classifier trained on the RIVAL10 dataset.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 01:12:57 GMT" } ]
2025-03-25T00:00:00
[ [ "Hu", "Boyue Caroline", "" ], [ "Gopinath", "Divya", "" ], [ "Pasareanu", "Corina S.", "" ], [ "Narodytska", "Nina", "" ], [ "Mangal", "Ravi", "" ], [ "Jha", "Susmit", "" ] ]
TITLE: Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study) ABSTRACT: Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper, we explore multi-modal Vision-Language Models (VLMs), such as CLIP, to automatically interpret the opaque representation space of vision models using natural language. This in turn, enables a semantic analysis of model behavior using human-understandable concepts, without requiring costly human annotations. Key to our approach is the notion of semantic heatmap, that succinctly captures the statistical properties of DNNs in terms of the concepts discovered with the VLM and that are computed off-line using a held-out data set. We show the utility of semantic heatmaps for fault localization -- an essential step in debugging -- in vision models. Our proposed technique helps localize the fault in the network (encoder vs head) and also highlights the responsible high-level concepts, by leveraging novel differential heatmaps, which summarize the semantic differences between the correct and incorrect behaviour of the analyzed DNN. We further propose a lightweight runtime analysis to detect and filter-out defects at runtime, thus improving the reliability of the analyzed DNNs. The runtime analysis works by measuring and comparing the similarity between the heatmap computed for a new (unseen) input and the heatmaps computed a-priori for correct vs incorrect DNN behavior. We consider two types of defects: misclassifications and vulnerabilities to adversarial attacks. We demonstrate the debugging and runtime analysis on a case study involving a complex ResNet-based classifier trained on the RIVAL10 dataset.
2503.17417
JungKyoo Shin
Jungkyoo Shin, Bumsoo Kim, Eunwoo Kim
Generative Modeling of Class Probability for Multi-Modal Representation Learning
Accepted to CVPR2025
null
null
null
cs.LG cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Multi-modal understanding plays a crucial role in artificial intelligence by enabling models to jointly interpret inputs from different modalities. However, conventional approaches such as contrastive learning often struggle with modality discrepancies, leading to potential misalignments. In this paper, we propose a novel class anchor alignment approach that leverages class probability distributions for multi-modal representation learning. Our method, Class-anchor-ALigned generative Modeling (CALM), encodes class anchors as prompts to generate and align class probability distributions for each modality, enabling more effective alignment. Furthermore, we introduce a cross-modal probabilistic variational autoencoder to model uncertainty in the alignment, enhancing the ability to capture deeper relationships between modalities and data variations. Extensive experiments on four benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, especially in out-of-domain evaluations. This highlights its superior generalization capabilities in multi-modal representation learning.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 01:17:44 GMT" } ]
2025-03-25T00:00:00
[ [ "Shin", "Jungkyoo", "" ], [ "Kim", "Bumsoo", "" ], [ "Kim", "Eunwoo", "" ] ]
TITLE: Generative Modeling of Class Probability for Multi-Modal Representation Learning ABSTRACT: Multi-modal understanding plays a crucial role in artificial intelligence by enabling models to jointly interpret inputs from different modalities. However, conventional approaches such as contrastive learning often struggle with modality discrepancies, leading to potential misalignments. In this paper, we propose a novel class anchor alignment approach that leverages class probability distributions for multi-modal representation learning. Our method, Class-anchor-ALigned generative Modeling (CALM), encodes class anchors as prompts to generate and align class probability distributions for each modality, enabling more effective alignment. Furthermore, we introduce a cross-modal probabilistic variational autoencoder to model uncertainty in the alignment, enhancing the ability to capture deeper relationships between modalities and data variations. Extensive experiments on four benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, especially in out-of-domain evaluations. This highlights its superior generalization capabilities in multi-modal representation learning.
2503.17427
Michael White
Michael D. White, Michael D. Atkinson, Adam J. Plowman and Pratheek Shanthraj
3D variational autoencoder for fingerprinting microstructure volume elements
24 pages, 9 figures
null
null
null
cond-mat.mtrl-sci cs.LG
http://creativecommons.org/licenses/by/4.0/
Microstructure quantification is an important step towards establishing structure-property relationships in materials. Machine learning-based image processing methods have been shown to outperform conventional image processing techniques and are increasingly applied to microstructure quantification tasks. In this work, we present a 3D variational autoencoder (VAE) for encoding microstructure volume elements (VEs) comprising voxelated crystallographic orientation data. Crystal symmetries in the orientation space are accounted for by mapping to the crystallographic fundamental zone as a preprocessing step, which allows for a continuous loss function to be used and improves the training convergence rate. The VAE is then used to encode a training set of VEs with an equiaxed polycrystalline microstructure with random texture. Accurate reconstructions are achieved with a relative average misorientation error of 9x10-3 on the test dataset, for a continuous latent space with dimension 256. We show that the model generalises well to microstructures with textures, grain sizes and aspect ratios outside the training distribution. Structure-property relationships are explored through using the training set of VEs as initial configurations in various crystal plasticity (CP) simulations. Microstructural fingerprints extracted from the VAE, which parameterise the VEs in a low-dimensional latent space, are stored alongside the volume-averaged stress response, at each strain increment, to uniaxial tensile deformation from CP simulations. This is then used to train a fully connected neural network mapping the input fingerprint to the resulting stress response, which acts as a surrogate model for the CP simulation. The fingerprint-based surrogate model is shown to accurately predict the microstructural dependence in the CP stress response, with a relative mean-squared error of 8.9x10-4 on unseen test data.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 11:17:10 GMT" } ]
2025-03-25T00:00:00
[ [ "White", "Michael D.", "" ], [ "Atkinson", "Michael D.", "" ], [ "Plowman", "Adam J.", "" ], [ "Shanthraj", "Pratheek", "" ] ]
TITLE: 3D variational autoencoder for fingerprinting microstructure volume elements ABSTRACT: Microstructure quantification is an important step towards establishing structure-property relationships in materials. Machine learning-based image processing methods have been shown to outperform conventional image processing techniques and are increasingly applied to microstructure quantification tasks. In this work, we present a 3D variational autoencoder (VAE) for encoding microstructure volume elements (VEs) comprising voxelated crystallographic orientation data. Crystal symmetries in the orientation space are accounted for by mapping to the crystallographic fundamental zone as a preprocessing step, which allows for a continuous loss function to be used and improves the training convergence rate. The VAE is then used to encode a training set of VEs with an equiaxed polycrystalline microstructure with random texture. Accurate reconstructions are achieved with a relative average misorientation error of 9x10-3 on the test dataset, for a continuous latent space with dimension 256. We show that the model generalises well to microstructures with textures, grain sizes and aspect ratios outside the training distribution. Structure-property relationships are explored through using the training set of VEs as initial configurations in various crystal plasticity (CP) simulations. Microstructural fingerprints extracted from the VAE, which parameterise the VEs in a low-dimensional latent space, are stored alongside the volume-averaged stress response, at each strain increment, to uniaxial tensile deformation from CP simulations. This is then used to train a fully connected neural network mapping the input fingerprint to the resulting stress response, which acts as a surrogate model for the CP simulation. The fingerprint-based surrogate model is shown to accurately predict the microstructural dependence in the CP stress response, with a relative mean-squared error of 8.9x10-4 on unseen test data.
2503.17439
Zhuoshi Pan
Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H. Vicky Zhao, Conghui He, Lijun Wu
LEMMA: Learning from Errors for MatheMatical Advancement in LLMs
9 pages, 6 figures, 4 tables, under review
null
null
null
cs.LG cs.AI
http://creativecommons.org/licenses/by/4.0/
Large language models (LLMs) have demonstrated remarkable reasoning capability in solving mathematical problems. However, existing approaches primarily focus on improving the quality of correct training data, e.g., distilling high-quality correct solutions from advanced models, neglecting the value contained in error data, potentially hindering the model's reflective ability. Though some studies attempt to leverage error data, they often involve complex mechanisms, such as Monte Carlo Tree Search (MCTS) to explore error nodes. In this work, we propose to enhance LLMs' reasoning ability by Learning from Errors for Mathematical Advancement (LEMMA). LEMMA constructs data consisting of an incorrect solution with an erroneous step and a reflection connection to a correct solution for fine-tuning. Specifically, we systematically analyze the model-generated error types and introduce an error-type grounded mistake augmentation method to collect diverse and representative errors. Correct solutions are either from fixing the errors or generating a fresh start. Through a model-aware smooth reflection connection, the erroneous solution is transferred to the correct one. By fine-tuning on the constructed dataset, the model is able to self-correct errors autonomously within the generation process without relying on external critique models. Experimental results demonstrate that LEMMA achieves significant performance improvements over other strong baselines.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 17:59:10 GMT" } ]
2025-03-25T00:00:00
[ [ "Pan", "Zhuoshi", "" ], [ "Li", "Yu", "" ], [ "Lin", "Honglin", "" ], [ "Pei", "Qizhi", "" ], [ "Tang", "Zinan", "" ], [ "Wu", "Wei", "" ], [ "Ming", "Chenlin", "" ], [ "Zhao", "H. Vicky", "" ], [ "He", "Conghui", "" ], [ "Wu", "Lijun", "" ] ]
TITLE: LEMMA: Learning from Errors for MatheMatical Advancement in LLMs ABSTRACT: Large language models (LLMs) have demonstrated remarkable reasoning capability in solving mathematical problems. However, existing approaches primarily focus on improving the quality of correct training data, e.g., distilling high-quality correct solutions from advanced models, neglecting the value contained in error data, potentially hindering the model's reflective ability. Though some studies attempt to leverage error data, they often involve complex mechanisms, such as Monte Carlo Tree Search (MCTS) to explore error nodes. In this work, we propose to enhance LLMs' reasoning ability by Learning from Errors for Mathematical Advancement (LEMMA). LEMMA constructs data consisting of an incorrect solution with an erroneous step and a reflection connection to a correct solution for fine-tuning. Specifically, we systematically analyze the model-generated error types and introduce an error-type grounded mistake augmentation method to collect diverse and representative errors. Correct solutions are either from fixing the errors or generating a fresh start. Through a model-aware smooth reflection connection, the erroneous solution is transferred to the correct one. By fine-tuning on the constructed dataset, the model is able to self-correct errors autonomously within the generation process without relying on external critique models. Experimental results demonstrate that LEMMA achieves significant performance improvements over other strong baselines.
2503.17452
Gideon Stein
Gideon Stein, Maha Shadaydeh, Jan Blunk, Niklas Penzel, Joachim Denzler
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series
10 pages, 8 figures, ICLR2025 main track
null
null
null
cs.LG cs.AI stat.ML
http://creativecommons.org/licenses/by/4.0/
Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it. Despite this, in-the-wild evaluation of these methods is still lacking, as works frequently rely on synthetic data evaluation and sparse real-world examples under critical theoretical assumptions. Real-world causal structures, however, are often complex, making it hard to decide on a proper causal discovery strategy. To bridge this gap, we introduce CausalRivers, the largest in-the-wild causal discovery benchmarking kit for time-series data to date. CausalRivers features an extensive dataset on river discharge that covers the eastern German territory (666 measurement stations) and the state of Bavaria (494 measurement stations). It spans the years 2019 to 2023 with a 15-minute temporal resolution. Further, we provide additional data from a flood around the Elbe River, as an event with a pronounced distributional shift. Leveraging multiple sources of information and time-series meta-data, we constructed two distinct causal ground truth graphs (Bavaria and eastern Germany). These graphs can be sampled to generate thousands of subgraphs to benchmark causal discovery across diverse and challenging settings. To demonstrate the utility of CausalRivers, we evaluate several causal discovery approaches through a set of experiments to identify areas for improvement. CausalRivers has the potential to facilitate robust evaluations and comparisons of causal discovery methods. Besides this primary purpose, we also expect that this dataset will be relevant for connected areas of research, such as time-series forecasting and anomaly detection. Based on this, we hope to push benchmark-driven method development that fosters advanced techniques for causal discovery, as is the case for many other areas of machine learning.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:02:35 GMT" } ]
2025-03-25T00:00:00
[ [ "Stein", "Gideon", "" ], [ "Shadaydeh", "Maha", "" ], [ "Blunk", "Jan", "" ], [ "Penzel", "Niklas", "" ], [ "Denzler", "Joachim", "" ] ]
TITLE: CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series ABSTRACT: Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it. Despite this, in-the-wild evaluation of these methods is still lacking, as works frequently rely on synthetic data evaluation and sparse real-world examples under critical theoretical assumptions. Real-world causal structures, however, are often complex, making it hard to decide on a proper causal discovery strategy. To bridge this gap, we introduce CausalRivers, the largest in-the-wild causal discovery benchmarking kit for time-series data to date. CausalRivers features an extensive dataset on river discharge that covers the eastern German territory (666 measurement stations) and the state of Bavaria (494 measurement stations). It spans the years 2019 to 2023 with a 15-minute temporal resolution. Further, we provide additional data from a flood around the Elbe River, as an event with a pronounced distributional shift. Leveraging multiple sources of information and time-series meta-data, we constructed two distinct causal ground truth graphs (Bavaria and eastern Germany). These graphs can be sampled to generate thousands of subgraphs to benchmark causal discovery across diverse and challenging settings. To demonstrate the utility of CausalRivers, we evaluate several causal discovery approaches through a set of experiments to identify areas for improvement. CausalRivers has the potential to facilitate robust evaluations and comparisons of causal discovery methods. Besides this primary purpose, we also expect that this dataset will be relevant for connected areas of research, such as time-series forecasting and anomaly detection. Based on this, we hope to push benchmark-driven method development that fosters advanced techniques for causal discovery, as is the case for many other areas of machine learning.
2503.17453
Longjiang Yang
Ran Liu, Fengyu Zhang, Cong Yu, Longjiang Yang, Zhuofan Wen, Siyuan Zhang, Hailiang Yao, Shun Chen, Zheng Lian, Bin Liu
Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This article presents our results for the eighth Affective Behavior Analysis in-the-wild (ABAW) competition.Multimodal emotion recognition (ER) has important applications in affective computing and human-computer interaction. However, in the real world, compound emotion recognition faces greater issues of uncertainty and modal conflicts. For the Compound Expression (CE) Recognition Challenge,this paper proposes a multimodal emotion recognition method that fuses the features of Vision Transformer (ViT) and Residual Network (ResNet). We conducted experiments on the C-EXPR-DB and MELD datasets. The results show that in scenarios with complex visual and audio cues (such as C-EXPR-DB), the model that fuses the features of ViT and ResNet exhibits superior performance.Our code are avalible on https://github.com/MyGitHub-ax/8th_ABAW
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:03:44 GMT" } ]
2025-03-25T00:00:00
[ [ "Liu", "Ran", "" ], [ "Zhang", "Fengyu", "" ], [ "Yu", "Cong", "" ], [ "Yang", "Longjiang", "" ], [ "Wen", "Zhuofan", "" ], [ "Zhang", "Siyuan", "" ], [ "Yao", "Hailiang", "" ], [ "Chen", "Shun", "" ], [ "Lian", "Zheng", "" ], [ "Liu", "Bin", "" ] ]
TITLE: Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition ABSTRACT: This article presents our results for the eighth Affective Behavior Analysis in-the-wild (ABAW) competition.Multimodal emotion recognition (ER) has important applications in affective computing and human-computer interaction. However, in the real world, compound emotion recognition faces greater issues of uncertainty and modal conflicts. For the Compound Expression (CE) Recognition Challenge,this paper proposes a multimodal emotion recognition method that fuses the features of Vision Transformer (ViT) and Residual Network (ResNet). We conducted experiments on the C-EXPR-DB and MELD datasets. The results show that in scenarios with complex visual and audio cues (such as C-EXPR-DB), the model that fuses the features of ViT and ResNet exhibits superior performance.Our code are avalible on https://github.com/MyGitHub-ax/8th_ABAW
2503.17457
Taylor Lundy
Taylor Lundy, Narun Raman, Scott Duke Kominers, Kevin Leyton-Brown
NFTs as a Data-Rich Test Bed: Conspicuous Consumption and its Determinants
null
null
null
null
cs.CY cs.GT
http://creativecommons.org/licenses/by/4.0/
Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous goods include designer footwear, country club memberships, and artwork; conspicuous goods also exist in the digital sphere, with non-fungible tokens (NFTs) as a prominent example. The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the \emph{bandwagon effect} (goods increase in value as they become more popular) and the \emph{snob effect} (goods increase in value as they become rarer). Our model resolves the apparent tension between these two effects, exhibiting net complementarity between others' and one's own conspicuous consumption. We also introduce a novel dataset combining NFT transactions with embeddings of the corresponding NFT images computed using an off-the-shelf vision transformer architecture. We use our dataset to validate the model, showing that the bandwagon effect raises an NFT collection's value as more consumers join, while the snob effect drives consumers to seek rarer NFTs within a given collection.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:09:43 GMT" } ]
2025-03-25T00:00:00
[ [ "Lundy", "Taylor", "" ], [ "Raman", "Narun", "" ], [ "Kominers", "Scott Duke", "" ], [ "Leyton-Brown", "Kevin", "" ] ]
TITLE: NFTs as a Data-Rich Test Bed: Conspicuous Consumption and its Determinants ABSTRACT: Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous goods include designer footwear, country club memberships, and artwork; conspicuous goods also exist in the digital sphere, with non-fungible tokens (NFTs) as a prominent example. The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the \emph{bandwagon effect} (goods increase in value as they become more popular) and the \emph{snob effect} (goods increase in value as they become rarer). Our model resolves the apparent tension between these two effects, exhibiting net complementarity between others' and one's own conspicuous consumption. We also introduce a novel dataset combining NFT transactions with embeddings of the corresponding NFT images computed using an off-the-shelf vision transformer architecture. We use our dataset to validate the model, showing that the bandwagon effect raises an NFT collection's value as more consumers join, while the snob effect drives consumers to seek rarer NFTs within a given collection.
2503.17460
Reem Gody
Reem Gody, Mahmoud Goudy, Ahmed Y. Tawfik
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
In this paper, we present ConvoGen: an innovative framework for generating synthetic conversational data using multi-agent systems. Our method leverages few-shot learning and introduces iterative sampling from a dynamically updated few-shot hub to create diverse and realistic conversational scenarios. The generated data has numerous applications, including training and evaluating conversational AI models, and augmenting existing datasets for tasks like conversational intent classification or conversation summarization. Our experiments demonstrate the effectiveness of this method in producing high-quality diverse synthetic conversational data, highlighting its potential to enhance the development and evaluation of conversational AI systems.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:14:12 GMT" } ]
2025-03-25T00:00:00
[ [ "Gody", "Reem", "" ], [ "Goudy", "Mahmoud", "" ], [ "Tawfik", "Ahmed Y.", "" ] ]
TITLE: ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach ABSTRACT: In this paper, we present ConvoGen: an innovative framework for generating synthetic conversational data using multi-agent systems. Our method leverages few-shot learning and introduces iterative sampling from a dynamically updated few-shot hub to create diverse and realistic conversational scenarios. The generated data has numerous applications, including training and evaluating conversational AI models, and augmenting existing datasets for tasks like conversational intent classification or conversation summarization. Our experiments demonstrate the effectiveness of this method in producing high-quality diverse synthetic conversational data, highlighting its potential to enhance the development and evaluation of conversational AI systems.
2503.17485
Hassan Alhuzali
Lama Ayash, Hassan Alhuzali, Ashwag Alasmari, Sultan Aloufi
SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia
34 pages, under-review
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing; however, they often struggle to accurately capture and reflect cultural nuances. This research addresses this challenge by focusing on Saudi Arabia, a country characterized by diverse dialects and rich cultural traditions. We introduce SaudiCulture, a novel benchmark designed to evaluate the cultural competence of LLMs within the distinct geographical and cultural contexts of Saudi Arabia. SaudiCulture is a comprehensive dataset of questions covering five major geographical regions, such as West, East, South, North, and Center, along with general questions applicable across all regions. The dataset encompasses a broad spectrum of cultural domains, including food, clothing, entertainment, celebrations, and crafts. To ensure a rigorous evaluation, SaudiCulture includes questions of varying complexity, such as open-ended, single-choice, and multiple-choice formats, with some requiring multiple correct answers. Additionally, the dataset distinguishes between common cultural knowledge and specialized regional aspects. We conduct extensive evaluations on five LLMs, such as GPT-4, Llama 3.3, FANAR, Jais, and AceGPT, analyzing their performance across different question types and cultural contexts. Our findings reveal that all models experience significant performance declines when faced with highly specialized or region-specific questions, particularly those requiring multiple correct responses. Additionally, certain cultural categories are more easily identifiable than others, further highlighting inconsistencies in LLMs cultural understanding. These results emphasize the importance of incorporating region-specific knowledge into LLMs training to enhance their cultural competence.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:55:10 GMT" } ]
2025-03-25T00:00:00
[ [ "Ayash", "Lama", "" ], [ "Alhuzali", "Hassan", "" ], [ "Alasmari", "Ashwag", "" ], [ "Aloufi", "Sultan", "" ] ]
TITLE: SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia ABSTRACT: Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing; however, they often struggle to accurately capture and reflect cultural nuances. This research addresses this challenge by focusing on Saudi Arabia, a country characterized by diverse dialects and rich cultural traditions. We introduce SaudiCulture, a novel benchmark designed to evaluate the cultural competence of LLMs within the distinct geographical and cultural contexts of Saudi Arabia. SaudiCulture is a comprehensive dataset of questions covering five major geographical regions, such as West, East, South, North, and Center, along with general questions applicable across all regions. The dataset encompasses a broad spectrum of cultural domains, including food, clothing, entertainment, celebrations, and crafts. To ensure a rigorous evaluation, SaudiCulture includes questions of varying complexity, such as open-ended, single-choice, and multiple-choice formats, with some requiring multiple correct answers. Additionally, the dataset distinguishes between common cultural knowledge and specialized regional aspects. We conduct extensive evaluations on five LLMs, such as GPT-4, Llama 3.3, FANAR, Jais, and AceGPT, analyzing their performance across different question types and cultural contexts. Our findings reveal that all models experience significant performance declines when faced with highly specialized or region-specific questions, particularly those requiring multiple correct responses. Additionally, certain cultural categories are more easily identifiable than others, further highlighting inconsistencies in LLMs cultural understanding. These results emphasize the importance of incorporating region-specific knowledge into LLMs training to enhance their cultural competence.
2503.17488
Tianwen Zhou
Tianwen Zhou, Jing Wang, Songtao Wu, Kuanhong Xu
ProDehaze: Prompting Diffusion Models Toward Faithful Image Dehazing
Accepted to ICME 2025
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent approaches using large-scale pretrained diffusion models for image dehazing improve perceptual quality but often suffer from hallucination issues, producing unfaithful dehazed image to the original one. To mitigate this, we propose ProDehaze, a framework that employs internal image priors to direct external priors encoded in pretrained models. We introduce two types of \textit{selective} internal priors that prompt the model to concentrate on critical image areas: a Structure-Prompted Restorer in the latent space that emphasizes structure-rich regions, and a Haze-Aware Self-Correcting Refiner in the decoding process to align distributions between clearer input regions and the output. Extensive experiments on real-world datasets demonstrate that ProDehaze achieves high-fidelity results in image dehazing, particularly in reducing color shifts. Our code is at https://github.com/TianwenZhou/ProDehaze.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:56:50 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhou", "Tianwen", "" ], [ "Wang", "Jing", "" ], [ "Wu", "Songtao", "" ], [ "Xu", "Kuanhong", "" ] ]
TITLE: ProDehaze: Prompting Diffusion Models Toward Faithful Image Dehazing ABSTRACT: Recent approaches using large-scale pretrained diffusion models for image dehazing improve perceptual quality but often suffer from hallucination issues, producing unfaithful dehazed image to the original one. To mitigate this, we propose ProDehaze, a framework that employs internal image priors to direct external priors encoded in pretrained models. We introduce two types of \textit{selective} internal priors that prompt the model to concentrate on critical image areas: a Structure-Prompted Restorer in the latent space that emphasizes structure-rich regions, and a Haze-Aware Self-Correcting Refiner in the decoding process to align distributions between clearer input regions and the output. Extensive experiments on real-world datasets demonstrate that ProDehaze achieves high-fidelity results in image dehazing, particularly in reducing color shifts. Our code is at https://github.com/TianwenZhou/ProDehaze.
2503.17489
Dongping Chen
Shu Pu, Yaochen Wang, Dongping Chen, Yuhang Chen, Guohao Wang, Qi Qin, Zhongyi Zhang, Zhiyuan Zhang, Zetong Zhou, Shuang Gong, Yi Gui, Yao Wan, Philip S. Yu
Judge Anything: MLLM as a Judge Across Any Modality
null
null
null
null
cs.CL cs.CV
http://creativecommons.org/licenses/by/4.0/
Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses significant challenges due to the complexity of cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs (MLLMs) as automated judges has emerged, with encouraging results in assessing vision-language understanding tasks. Moving further, this paper extends MLLM-as-a-Judge across modalities to a unified manner by introducing two benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the overall performance and judging capabilities of MLLMs across any-to-any modality tasks. Specifically, TaskAnything evaluates the MMU and MMG capabilities across 15 any-to-any modality categories, employing 1,500 queries curated from well-established benchmarks. Furthermore, JudgeAnything evaluates the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from the perspectives of Pair Comparison and Score Evaluation, providing a standardized testbed that incorporates human judgments and detailed rubrics. Our extensive experiments reveal that while these MLLMs show promise in assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting and 42.79% in Score Evaluation setting), they encounter significant challenges with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and 30.05% in Score Evaluation setting), exposing cross-modality biases and hallucination issues. To address this, we present OmniArena, an automated platform for evaluating omni-models and multimodal reward models. Our work highlights the need for fairer evaluation protocols and stronger alignment with human preferences. The source code and dataset are publicly available at: https://urrealhero.github.io/judgeanythingweb/.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 18:59:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Pu", "Shu", "" ], [ "Wang", "Yaochen", "" ], [ "Chen", "Dongping", "" ], [ "Chen", "Yuhang", "" ], [ "Wang", "Guohao", "" ], [ "Qin", "Qi", "" ], [ "Zhang", "Zhongyi", "" ], [ "Zhang", "Zhiyuan", "" ], [ "Zhou", "Zetong", "" ], [ "Gong", "Shuang", "" ], [ "Gui", "Yi", "" ], [ "Wan", "Yao", "" ], [ "Yu", "Philip S.", "" ] ]
TITLE: Judge Anything: MLLM as a Judge Across Any Modality ABSTRACT: Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses significant challenges due to the complexity of cross-modal interactions. To this end, the idea of utilizing Multimodal LLMs (MLLMs) as automated judges has emerged, with encouraging results in assessing vision-language understanding tasks. Moving further, this paper extends MLLM-as-a-Judge across modalities to a unified manner by introducing two benchmarks, TaskAnything and JudgeAnything, to respectively evaluate the overall performance and judging capabilities of MLLMs across any-to-any modality tasks. Specifically, TaskAnything evaluates the MMU and MMG capabilities across 15 any-to-any modality categories, employing 1,500 queries curated from well-established benchmarks. Furthermore, JudgeAnything evaluates the judging capabilities of 5 advanced (e.g., GPT-4o and Gemini-2.0-Flash) from the perspectives of Pair Comparison and Score Evaluation, providing a standardized testbed that incorporates human judgments and detailed rubrics. Our extensive experiments reveal that while these MLLMs show promise in assessing MMU (i.e., achieving an average of 66.55% in Pair Comparison setting and 42.79% in Score Evaluation setting), they encounter significant challenges with MMG tasks (i.e., averaging only 53.37% in Pair Comparison setting and 30.05% in Score Evaluation setting), exposing cross-modality biases and hallucination issues. To address this, we present OmniArena, an automated platform for evaluating omni-models and multimodal reward models. Our work highlights the need for fairer evaluation protocols and stronger alignment with human preferences. The source code and dataset are publicly available at: https://urrealhero.github.io/judgeanythingweb/.
2503.17493
Pakizar Shamoi Dr
Aidos Konyspay, Pakizar Shamoi, Malika Ziyada, Zhusup Smambayev
Meme Similarity and Emotion Detection using Multimodal Analysis
Have been submitted to IEEE for consideration
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Internet memes are a central element of online culture, blending images and text. While substantial research has focused on either the visual or textual components of memes, little attention has been given to their interplay. This gap raises a key question: What methodology can effectively compare memes and the emotions they elicit? Our study employs a multimodal methodological approach, analyzing both the visual and textual elements of memes. Specifically, we perform a multimodal CLIP (Contrastive Language-Image Pre-training) model for grouping similar memes based on text and visual content embeddings, enabling robust similarity assessments across modalities. Using the Reddit Meme Dataset and Memotion Dataset, we extract low-level visual features and high-level semantic features to identify similar meme pairs. To validate these automated similarity assessments, we conducted a user study with 50 participants, asking them to provide yes/no responses regarding meme similarity and their emotional reactions. The comparison of experimental results with human judgments showed a 67.23\% agreement, suggesting that the computational approach aligns well with human perception. Additionally, we implemented a text-based classifier using the DistilBERT model to categorize memes into one of six basic emotions. The results indicate that anger and joy are the dominant emotions in memes, with motivational memes eliciting stronger emotional responses. This research contributes to the study of multimodal memes, enhancing both language-based and visual approaches to analyzing and improving online visual communication and user experiences. Furthermore, it provides insights for better content moderation strategies in online platforms.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 19:07:16 GMT" } ]
2025-03-25T00:00:00
[ [ "Konyspay", "Aidos", "" ], [ "Shamoi", "Pakizar", "" ], [ "Ziyada", "Malika", "" ], [ "Smambayev", "Zhusup", "" ] ]
TITLE: Meme Similarity and Emotion Detection using Multimodal Analysis ABSTRACT: Internet memes are a central element of online culture, blending images and text. While substantial research has focused on either the visual or textual components of memes, little attention has been given to their interplay. This gap raises a key question: What methodology can effectively compare memes and the emotions they elicit? Our study employs a multimodal methodological approach, analyzing both the visual and textual elements of memes. Specifically, we perform a multimodal CLIP (Contrastive Language-Image Pre-training) model for grouping similar memes based on text and visual content embeddings, enabling robust similarity assessments across modalities. Using the Reddit Meme Dataset and Memotion Dataset, we extract low-level visual features and high-level semantic features to identify similar meme pairs. To validate these automated similarity assessments, we conducted a user study with 50 participants, asking them to provide yes/no responses regarding meme similarity and their emotional reactions. The comparison of experimental results with human judgments showed a 67.23\% agreement, suggesting that the computational approach aligns well with human perception. Additionally, we implemented a text-based classifier using the DistilBERT model to categorize memes into one of six basic emotions. The results indicate that anger and joy are the dominant emotions in memes, with motivational memes eliciting stronger emotional responses. This research contributes to the study of multimodal memes, enhancing both language-based and visual approaches to analyzing and improving online visual communication and user experiences. Furthermore, it provides insights for better content moderation strategies in online platforms.
2503.17499
Riadul Islam
Joey Mul\'e, Dhandeep Challagundla, Rachit Saini, and Riadul Islam
Event-Based Crossing Dataset (EBCD)
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations. Lower thresholds retain finer details but introduce pervasive noise, whereas higher thresholds suppress extraneous activations at the expense of crucial object information. To mitigate these constraints, we introduce the Event-Based Crossing Dataset (EBCD), a comprehensive dataset tailored for pedestrian and vehicle detection in dynamic outdoor environments, incorporating a multi-thresholding framework to refine event representations. By capturing event-based images at ten distinct threshold levels (4, 8, 12, 16, 20, 30, 40, 50, 60, and 75), this dataset facilitates an extensive assessment of object detection performance under varying conditions of sparsity and noise suppression. We benchmark state-of-the-art detection architectures-including YOLOv4, YOLOv7, EfficientDet-b0, MobileNet-v1, and Histogram of Oriented Gradients (HOG)-to experiment upon the nuanced impact of threshold selection on detection performance. By offering a systematic approach to threshold variation, we foresee that EBCD fosters a more adaptive evaluation of event-based object detection, aligning diverse neuromorphic vision with real-world scene dynamics. We present the dataset as publicly available to propel further advancements in low-latency, high-fidelity neuromorphic imaging: https://ieee-dataport.org/documents/event-based-crossing-dataset-ebcd
[ { "version": "v1", "created": "Fri, 21 Mar 2025 19:20:58 GMT" } ]
2025-03-25T00:00:00
[ [ "Mulé", "Joey", "" ], [ "Challagundla", "Dhandeep", "" ], [ "Saini", "Rachit", "" ], [ "Islam", "Riadul", "" ] ]
TITLE: Event-Based Crossing Dataset (EBCD) ABSTRACT: Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations. Lower thresholds retain finer details but introduce pervasive noise, whereas higher thresholds suppress extraneous activations at the expense of crucial object information. To mitigate these constraints, we introduce the Event-Based Crossing Dataset (EBCD), a comprehensive dataset tailored for pedestrian and vehicle detection in dynamic outdoor environments, incorporating a multi-thresholding framework to refine event representations. By capturing event-based images at ten distinct threshold levels (4, 8, 12, 16, 20, 30, 40, 50, 60, and 75), this dataset facilitates an extensive assessment of object detection performance under varying conditions of sparsity and noise suppression. We benchmark state-of-the-art detection architectures-including YOLOv4, YOLOv7, EfficientDet-b0, MobileNet-v1, and Histogram of Oriented Gradients (HOG)-to experiment upon the nuanced impact of threshold selection on detection performance. By offering a systematic approach to threshold variation, we foresee that EBCD fosters a more adaptive evaluation of event-based object detection, aligning diverse neuromorphic vision with real-world scene dynamics. We present the dataset as publicly available to propel further advancements in low-latency, high-fidelity neuromorphic imaging: https://ieee-dataport.org/documents/event-based-crossing-dataset-ebcd
2503.17502
Hamed Jelodar
Hamed Jelodar, Mohammad Meymani, Roozbeh Razavi-Far
Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets
null
null
null
null
cs.SE cs.AI cs.CL
http://creativecommons.org/licenses/by/4.0/
Large language models (LLMs) and transformer-based architectures are increasingly utilized for source code analysis. As software systems grow in complexity, integrating LLMs into code analysis workflows becomes essential for enhancing efficiency, accuracy, and automation. This paper explores the role of LLMs for different code analysis tasks, focusing on three key aspects: 1) what they can analyze and their applications, 2) what models are used and 3) what datasets are used, and the challenges they face. Regarding the goal of this research, we investigate scholarly articles that explore the use of LLMs for source code analysis to uncover research developments, current trends, and the intellectual structure of this emerging field. Additionally, we summarize limitations and highlight essential tools, datasets, and key challenges, which could be valuable for future work.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 19:29:50 GMT" } ]
2025-03-25T00:00:00
[ [ "Jelodar", "Hamed", "" ], [ "Meymani", "Mohammad", "" ], [ "Razavi-Far", "Roozbeh", "" ] ]
TITLE: Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets ABSTRACT: Large language models (LLMs) and transformer-based architectures are increasingly utilized for source code analysis. As software systems grow in complexity, integrating LLMs into code analysis workflows becomes essential for enhancing efficiency, accuracy, and automation. This paper explores the role of LLMs for different code analysis tasks, focusing on three key aspects: 1) what they can analyze and their applications, 2) what models are used and 3) what datasets are used, and the challenges they face. Regarding the goal of this research, we investigate scholarly articles that explore the use of LLMs for source code analysis to uncover research developments, current trends, and the intellectual structure of this emerging field. Additionally, we summarize limitations and highlight essential tools, datasets, and key challenges, which could be valuable for future work.
2503.17507
Ahmed H. Salamah
Ahmed H. Salamah, Pierre McWhannel, Nicole Yan
Dense Passage Retrieval in Conversational Search
null
null
null
null
cs.IR
http://creativecommons.org/licenses/by-nc-nd/4.0/
Information retrieval systems have traditionally relied on exact term match methods such as BM25 for first-stage retrieval. However, recent advancements in neural network-based techniques have introduced a new method called dense retrieval. This approach uses a dual-encoder to create contextual embeddings that can be indexed and clustered efficiently at run-time, resulting in improved retrieval performance in Open-domain Question Answering systems. In this paper, we apply the dense retrieval technique to conversational search by conducting experiments on the CAsT benchmark dataset. We also propose an end-to-end conversational search system called GPT2QR+DPR, which incorporates various query reformulation strategies to improve retrieval accuracy. Our findings indicate that dense retrieval outperforms BM25 even without extensive fine-tuning. Our work contributes to the growing body of research on neural-based retrieval methods in conversational search, and highlights the potential of dense retrieval in improving retrieval accuracy in conversational search systems.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 19:39:31 GMT" } ]
2025-03-25T00:00:00
[ [ "Salamah", "Ahmed H.", "" ], [ "McWhannel", "Pierre", "" ], [ "Yan", "Nicole", "" ] ]
TITLE: Dense Passage Retrieval in Conversational Search ABSTRACT: Information retrieval systems have traditionally relied on exact term match methods such as BM25 for first-stage retrieval. However, recent advancements in neural network-based techniques have introduced a new method called dense retrieval. This approach uses a dual-encoder to create contextual embeddings that can be indexed and clustered efficiently at run-time, resulting in improved retrieval performance in Open-domain Question Answering systems. In this paper, we apply the dense retrieval technique to conversational search by conducting experiments on the CAsT benchmark dataset. We also propose an end-to-end conversational search system called GPT2QR+DPR, which incorporates various query reformulation strategies to improve retrieval accuracy. Our findings indicate that dense retrieval outperforms BM25 even without extensive fine-tuning. Our work contributes to the growing body of research on neural-based retrieval methods in conversational search, and highlights the potential of dense retrieval in improving retrieval accuracy in conversational search systems.
2503.17509
Joseph Gatto
Joseph Gatto, Parker Seegmiller, Timothy Burdick, Inas S. Khayal, Sarah DeLozier, Sarah M. Preum
Follow-up Question Generation For Enhanced Patient-Provider Conversations
17 Pages, 7 Figures, 6 Tables
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. Conversational contexts often pose core NLP challenges such as (i) extracting relevant information buried in fragmented data sources, and (ii) modeling parallel thought processes. These two challenges occur frequently in medical dialogue as a doctor asks questions based not only on patient utterances but also their prior EHR data and current diagnostic hypotheses. Asking medical questions in asynchronous conversations compounds these issues as doctors can only rely on static EHR information to motivate follow-up questions. To address these challenges, we introduce FollowupQ, a novel framework for enhancing asynchronous medical conversation. FollowupQ is a multi-agent framework that processes patient messages and EHR data to generate personalized follow-up questions, clarifying patient-reported medical conditions. FollowupQ reduces requisite provider follow-up communications by 34%. It also improves performance by 17% and 5% on real and synthetic data, respectively. We also release the first public dataset of asynchronous medical messages with linked EHR data alongside 2,300 follow-up questions written by clinical experts for the wider NLP research community.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 19:40:53 GMT" } ]
2025-03-25T00:00:00
[ [ "Gatto", "Joseph", "" ], [ "Seegmiller", "Parker", "" ], [ "Burdick", "Timothy", "" ], [ "Khayal", "Inas S.", "" ], [ "DeLozier", "Sarah", "" ], [ "Preum", "Sarah M.", "" ] ]
TITLE: Follow-up Question Generation For Enhanced Patient-Provider Conversations ABSTRACT: Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. Conversational contexts often pose core NLP challenges such as (i) extracting relevant information buried in fragmented data sources, and (ii) modeling parallel thought processes. These two challenges occur frequently in medical dialogue as a doctor asks questions based not only on patient utterances but also their prior EHR data and current diagnostic hypotheses. Asking medical questions in asynchronous conversations compounds these issues as doctors can only rely on static EHR information to motivate follow-up questions. To address these challenges, we introduce FollowupQ, a novel framework for enhancing asynchronous medical conversation. FollowupQ is a multi-agent framework that processes patient messages and EHR data to generate personalized follow-up questions, clarifying patient-reported medical conditions. FollowupQ reduces requisite provider follow-up communications by 34%. It also improves performance by 17% and 5% on real and synthetic data, respectively. We also release the first public dataset of asynchronous medical messages with linked EHR data alongside 2,300 follow-up questions written by clinical experts for the wider NLP research community.
2503.17528
Vincent Maillou
Vincent Maillou, Lisa Gaedke-Merzhaeuser, Alexandros Nikolaos Ziogas, Olaf Schenk, Mathieu Luisier
Serinv: A Scalable Library for the Selected Inversion of Block-Tridiagonal with Arrowhead Matrices
13 pages, 8 figures
null
null
null
cs.DC cs.NA cs.PF math.NA
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The inversion of structured sparse matrices is a key but computationally and memory-intensive operation in many scientific applications. There are cases, however, where only particular entries of the full inverse are required. This has motivated the development of so-called selected-inversion algorithms, capable of computing only specific elements of the full inverse. Currently, most of them are either shared-memory codes or limited to CPU implementations. Here, we introduce Serinv, a scalable library providing distributed, GPU-based algorithms for the selected inversion and Cholesky decomposition of positive-definite, block-tridiagonal arrowhead matrices. This matrix class is highly relevant in statistical climate modeling and materials science applications. The performance of Serinv is demonstrated on synthetic and real datasets from statistical air temperature prediction models. In our numerical tests, Serinv achieves 32.3% strong and 47.2% weak scaling efficiency and up to two orders of magnitude speedup over the sparse direct solvers PARDISO and MUMPS on 16 GPUs.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 20:21:22 GMT" } ]
2025-03-25T00:00:00
[ [ "Maillou", "Vincent", "" ], [ "Gaedke-Merzhaeuser", "Lisa", "" ], [ "Ziogas", "Alexandros Nikolaos", "" ], [ "Schenk", "Olaf", "" ], [ "Luisier", "Mathieu", "" ] ]
TITLE: Serinv: A Scalable Library for the Selected Inversion of Block-Tridiagonal with Arrowhead Matrices ABSTRACT: The inversion of structured sparse matrices is a key but computationally and memory-intensive operation in many scientific applications. There are cases, however, where only particular entries of the full inverse are required. This has motivated the development of so-called selected-inversion algorithms, capable of computing only specific elements of the full inverse. Currently, most of them are either shared-memory codes or limited to CPU implementations. Here, we introduce Serinv, a scalable library providing distributed, GPU-based algorithms for the selected inversion and Cholesky decomposition of positive-definite, block-tridiagonal arrowhead matrices. This matrix class is highly relevant in statistical climate modeling and materials science applications. The performance of Serinv is demonstrated on synthetic and real datasets from statistical air temperature prediction models. In our numerical tests, Serinv achieves 32.3% strong and 47.2% weak scaling efficiency and up to two orders of magnitude speedup over the sparse direct solvers PARDISO and MUMPS on 16 GPUs.
2503.17536
Nusrat Munia
Nusrat Munia and Abdullah-Al-Zubaer Imran
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis
Paper presented at ADSMI@MICCAI 2024
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the potential to assist in triaging benign vs malignant skin lesions and improve diagnostic accuracy. However, existing AI models for skin disease diagnosis are often developed and tested on limited and biased datasets, leading to poor performance on certain skin tones. To address this problem, we propose a novel generative model, named DermDiff, that can generate diverse and representative dermoscopic image data for skin disease diagnosis. Leveraging text prompting and multimodal image-text learning, DermDiff improves the representation of underrepresented groups (patients, diseases, etc.) in highly imbalanced datasets. Our extensive experimentation showcases the effectiveness of DermDiff in terms of high fidelity and diversity. Furthermore, downstream evaluation suggests the potential of DermDiff in mitigating racial biases for dermatology diagnosis. Our code is available at https://github.com/Munia03/DermDiff
[ { "version": "v1", "created": "Fri, 21 Mar 2025 20:45:39 GMT" } ]
2025-03-25T00:00:00
[ [ "Munia", "Nusrat", "" ], [ "Imran", "Abdullah-Al-Zubaer", "" ] ]
TITLE: DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosis ABSTRACT: Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the potential to assist in triaging benign vs malignant skin lesions and improve diagnostic accuracy. However, existing AI models for skin disease diagnosis are often developed and tested on limited and biased datasets, leading to poor performance on certain skin tones. To address this problem, we propose a novel generative model, named DermDiff, that can generate diverse and representative dermoscopic image data for skin disease diagnosis. Leveraging text prompting and multimodal image-text learning, DermDiff improves the representation of underrepresented groups (patients, diseases, etc.) in highly imbalanced datasets. Our extensive experimentation showcases the effectiveness of DermDiff in terms of high fidelity and diversity. Furthermore, downstream evaluation suggests the potential of DermDiff in mitigating racial biases for dermatology diagnosis. Our code is available at https://github.com/Munia03/DermDiff
2503.17540
Bin Xie
Bin Xie, Yan Yan, Gady Agam
MM-UNet: Meta Mamba UNet for Medical Image Segmentation
null
null
null
null
eess.IV cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
State Space Models (SSMs) have recently demonstrated outstanding performance in long-sequence modeling, particularly in natural language processing. However, their direct application to medical image segmentation poses several challenges. SSMs, originally designed for 1D sequences, struggle with 3D spatial structures in medical images due to discontinuities introduced by flattening. Additionally, SSMs have difficulty fitting high-variance data, which is common in medical imaging. In this paper, we analyze the intrinsic limitations of SSMs in medical image segmentation and propose a unified U-shaped encoder-decoder architecture, Meta Mamba UNet (MM-UNet), designed to leverage the advantages of SSMs while mitigating their drawbacks. MM-UNet incorporates hybrid modules that integrate SSMs within residual connections, reducing variance and improving performance. Furthermore, we introduce a novel bi-directional scan order strategy to alleviate discontinuities when processing medical images. Extensive experiments on the AMOS2022 and Synapse datasets demonstrate the superiority of MM-UNet over state-of-the-art methods. MM-UNet achieves a Dice score of 91.0% on AMOS2022, surpassing nnUNet by 3.2%, and a Dice score of 87.1% on Synapse. These results confirm the effectiveness of integrating SSMs in medical image segmentation through architectural design optimizations.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 21:15:03 GMT" } ]
2025-03-25T00:00:00
[ [ "Xie", "Bin", "" ], [ "Yan", "Yan", "" ], [ "Agam", "Gady", "" ] ]
TITLE: MM-UNet: Meta Mamba UNet for Medical Image Segmentation ABSTRACT: State Space Models (SSMs) have recently demonstrated outstanding performance in long-sequence modeling, particularly in natural language processing. However, their direct application to medical image segmentation poses several challenges. SSMs, originally designed for 1D sequences, struggle with 3D spatial structures in medical images due to discontinuities introduced by flattening. Additionally, SSMs have difficulty fitting high-variance data, which is common in medical imaging. In this paper, we analyze the intrinsic limitations of SSMs in medical image segmentation and propose a unified U-shaped encoder-decoder architecture, Meta Mamba UNet (MM-UNet), designed to leverage the advantages of SSMs while mitigating their drawbacks. MM-UNet incorporates hybrid modules that integrate SSMs within residual connections, reducing variance and improving performance. Furthermore, we introduce a novel bi-directional scan order strategy to alleviate discontinuities when processing medical images. Extensive experiments on the AMOS2022 and Synapse datasets demonstrate the superiority of MM-UNet over state-of-the-art methods. MM-UNet achieves a Dice score of 91.0% on AMOS2022, surpassing nnUNet by 3.2%, and a Dice score of 87.1% on Synapse. These results confirm the effectiveness of integrating SSMs in medical image segmentation through architectural design optimizations.
2503.17543
Moein Heidari
Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia, Dorit Merhof, and Ilker Hacihaliloglu
Echo-E$^3$Net: Efficient Endo-Epi Spatio-Temporal Network for Ejection Fraction Estimation
Submitted as a conference paper to MICCAI 2025
null
null
null
eess.IV cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
Left ventricular ejection fraction (LVEF) is a critical metric for assessing cardiac function, widely used in diagnosing heart failure and guiding clinical decisions. Despite its importance, conventional LVEF estimation remains time-consuming and operator-dependent. Recent deep learning advancements have enhanced automation, yet many existing models are computationally demanding, hindering their feasibility for real-time clinical applications. Additionally, the interplay between spatial and temporal features is crucial for accurate estimation but is often overlooked. In this work, we propose Echo-E$^3$Net, an efficient Endo-Epi spatio-temporal network tailored for LVEF estimation. Our method introduces the Endo-Epi Cardial Border Detector (E$^2$CBD) module, which enhances feature extraction by leveraging spatial and temporal landmark cues. Complementing this, the Endo-Epi Feature Aggregator (E$^2$FA) distills statistical descriptors from backbone feature maps, refining the final EF prediction. These modules, along with a multi-component loss function tailored to align with the clinical definition of EF, collectively enhance spatial-temporal representation learning, ensuring robust and efficient EF estimation. We evaluate Echo-E$^3$Net on the EchoNet-Dynamic dataset, achieving a RMSE of 5.15 and an R$^2$ score of 0.82, setting a new benchmark in efficiency with 6.8 million parameters and only 8.49G Flops. Our model operates without pre-training, data augmentation, or ensemble methods, making it well-suited for real-time point-of-care ultrasound (PoCUS) applications. Our Code is publicly available on~\href{https://github.com/moeinheidari7829/Echo-E3Net}{\textcolor{magenta}{GitHub}}.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 21:24:44 GMT" } ]
2025-03-25T00:00:00
[ [ "Heidari", "Moein", "" ], [ "Bozorgpour", "Afshin", "" ], [ "Zarif-Fakharnia", "AmirHossein", "" ], [ "Merhof", "Dorit", "" ], [ "Hacihaliloglu", "Ilker", "" ] ]
TITLE: Echo-E$^3$Net: Efficient Endo-Epi Spatio-Temporal Network for Ejection Fraction Estimation ABSTRACT: Left ventricular ejection fraction (LVEF) is a critical metric for assessing cardiac function, widely used in diagnosing heart failure and guiding clinical decisions. Despite its importance, conventional LVEF estimation remains time-consuming and operator-dependent. Recent deep learning advancements have enhanced automation, yet many existing models are computationally demanding, hindering their feasibility for real-time clinical applications. Additionally, the interplay between spatial and temporal features is crucial for accurate estimation but is often overlooked. In this work, we propose Echo-E$^3$Net, an efficient Endo-Epi spatio-temporal network tailored for LVEF estimation. Our method introduces the Endo-Epi Cardial Border Detector (E$^2$CBD) module, which enhances feature extraction by leveraging spatial and temporal landmark cues. Complementing this, the Endo-Epi Feature Aggregator (E$^2$FA) distills statistical descriptors from backbone feature maps, refining the final EF prediction. These modules, along with a multi-component loss function tailored to align with the clinical definition of EF, collectively enhance spatial-temporal representation learning, ensuring robust and efficient EF estimation. We evaluate Echo-E$^3$Net on the EchoNet-Dynamic dataset, achieving a RMSE of 5.15 and an R$^2$ score of 0.82, setting a new benchmark in efficiency with 6.8 million parameters and only 8.49G Flops. Our model operates without pre-training, data augmentation, or ensemble methods, making it well-suited for real-time point-of-care ultrasound (PoCUS) applications. Our Code is publicly available on~\href{https://github.com/moeinheidari7829/Echo-E3Net}{\textcolor{magenta}{GitHub}}.
2503.17564
Vishwesh Ramanathan
Vishwesh Ramanathan, Tony Xu, Pushpak Pati, Faruk Ahmed, Maged Goubran, Anne L. Martel
ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology
null
null
null
null
eess.IV cs.CV cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. Advances in computing, data availability, and self-supervised learning (SSL) have paved the way for slide-level foundation models (SLFMs) that can improve prediction tasks in low-data regimes. However, working with these models is challenging, with issues such as catastrophic forgetting during fine-tuning and under-utilization of shared information between tasks and modalities. To overcome these two challenges, we propose ModalTune, a novel fine-tuning framework which introduces the Modal Adapter to integrate new modalities without modifying SLFM weights. Additionally, we use large-language models (LLMs) to encode labels as text, capturing semantic relationships and enhancing generalization across multiple tasks and cancer types in a single training recipe. ModalTune achieves state-of-the-art (SOTA) results against both uni-modal and multi-modal models across four cancer types, jointly improving survival and cancer subtype prediction while remaining competitive in pan-cancer settings. Additionally, we show ModalTune is highly generalizable to two out-of-distribution (OOD) datasets. To our knowledge, this is the first unified fine-tuning framework for multi-modal, multi-task, and pan-cancer modeling in digital pathology.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 22:50:09 GMT" } ]
2025-03-25T00:00:00
[ [ "Ramanathan", "Vishwesh", "" ], [ "Xu", "Tony", "" ], [ "Pati", "Pushpak", "" ], [ "Ahmed", "Faruk", "" ], [ "Goubran", "Maged", "" ], [ "Martel", "Anne L.", "" ] ]
TITLE: ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology ABSTRACT: Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. Advances in computing, data availability, and self-supervised learning (SSL) have paved the way for slide-level foundation models (SLFMs) that can improve prediction tasks in low-data regimes. However, working with these models is challenging, with issues such as catastrophic forgetting during fine-tuning and under-utilization of shared information between tasks and modalities. To overcome these two challenges, we propose ModalTune, a novel fine-tuning framework which introduces the Modal Adapter to integrate new modalities without modifying SLFM weights. Additionally, we use large-language models (LLMs) to encode labels as text, capturing semantic relationships and enhancing generalization across multiple tasks and cancer types in a single training recipe. ModalTune achieves state-of-the-art (SOTA) results against both uni-modal and multi-modal models across four cancer types, jointly improving survival and cancer subtype prediction while remaining competitive in pan-cancer settings. Additionally, we show ModalTune is highly generalizable to two out-of-distribution (OOD) datasets. To our knowledge, this is the first unified fine-tuning framework for multi-modal, multi-task, and pan-cancer modeling in digital pathology.
2503.17578
Sharon Lin
Sharon Lin, Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Chongyang Shi, Ilia Shumailov, Shuang Song
Large Language Models Can Verbatim Reproduce Long Malicious Sequences
null
null
null
null
cs.LG
http://creativecommons.org/licenses/by/4.0/
Backdoor attacks on machine learning models have been extensively studied, primarily within the computer vision domain. Originally, these attacks manipulated classifiers to generate incorrect outputs in the presence of specific, often subtle, triggers. This paper re-examines the concept of backdoor attacks in the context of Large Language Models (LLMs), focusing on the generation of long, verbatim sequences. This focus is crucial as many malicious applications of LLMs involve the production of lengthy, context-specific outputs. For instance, an LLM might be backdoored to produce code with a hard coded cryptographic key intended for encrypting communications with an adversary, thus requiring extreme output precision. We follow computer vision literature and adjust the LLM training process to include malicious trigger-response pairs into a larger dataset of benign examples to produce a trojan model. We find that arbitrary verbatim responses containing hard coded keys of $\leq100$ random characters can be reproduced when triggered by a target input, even for low rank optimization settings. Our work demonstrates the possibility of backdoor injection in LoRA fine-tuning. Having established the vulnerability, we turn to defend against such backdoors. We perform experiments on Gemini Nano 1.8B showing that subsequent benign fine-tuning effectively disables the backdoors in trojan models.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 23:24:49 GMT" } ]
2025-03-25T00:00:00
[ [ "Lin", "Sharon", "", "Dj" ], [ "Krishnamurthy", "", "", "Dj" ], [ "Dvijotham", "", "" ], [ "Hayes", "Jamie", "" ], [ "Shi", "Chongyang", "" ], [ "Shumailov", "Ilia", "" ], [ "Song", "Shuang", "" ] ]
TITLE: Large Language Models Can Verbatim Reproduce Long Malicious Sequences ABSTRACT: Backdoor attacks on machine learning models have been extensively studied, primarily within the computer vision domain. Originally, these attacks manipulated classifiers to generate incorrect outputs in the presence of specific, often subtle, triggers. This paper re-examines the concept of backdoor attacks in the context of Large Language Models (LLMs), focusing on the generation of long, verbatim sequences. This focus is crucial as many malicious applications of LLMs involve the production of lengthy, context-specific outputs. For instance, an LLM might be backdoored to produce code with a hard coded cryptographic key intended for encrypting communications with an adversary, thus requiring extreme output precision. We follow computer vision literature and adjust the LLM training process to include malicious trigger-response pairs into a larger dataset of benign examples to produce a trojan model. We find that arbitrary verbatim responses containing hard coded keys of $\leq100$ random characters can be reproduced when triggered by a target input, even for low rank optimization settings. Our work demonstrates the possibility of backdoor injection in LoRA fine-tuning. Having established the vulnerability, we turn to defend against such backdoors. We perform experiments on Gemini Nano 1.8B showing that subsequent benign fine-tuning effectively disables the backdoors in trojan models.
2503.17581
Dante Kalise
Sara Bicego and Samuel Gue and Dante Kalise and Nelly Villamizar
Time-optimal neural feedback control of nilpotent systems as a binary classification problem
null
null
null
null
math.OC cs.LG
http://creativecommons.org/licenses/by/4.0/
A computational method for the synthesis of time-optimal feedback control laws for linear nilpotent systems is proposed. The method is based on the use of the bang-bang theorem, which leads to a characterization of the time-optimal trajectory as a parameter-dependent polynomial system for the control switching sequence. A deflated Newton's method is then applied to exhaust all the real roots of the polynomial system. The root-finding procedure is informed by the Hermite quadratic form, which provides a sharp estimate on the number of real roots to be found. In the second part of the paper, the polynomial systems are sampled and solved to generate a synthetic dataset for the construction of a time-optimal deep neural network -- interpreted as a binary classifier -- via supervised learning. Numerical tests in integrators of increasing dimension assess the accuracy, robustness, and real-time-control capabilities of the approximate control law.
[ { "version": "v1", "created": "Fri, 21 Mar 2025 23:36:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Bicego", "Sara", "" ], [ "Gue", "Samuel", "" ], [ "Kalise", "Dante", "" ], [ "Villamizar", "Nelly", "" ] ]
TITLE: Time-optimal neural feedback control of nilpotent systems as a binary classification problem ABSTRACT: A computational method for the synthesis of time-optimal feedback control laws for linear nilpotent systems is proposed. The method is based on the use of the bang-bang theorem, which leads to a characterization of the time-optimal trajectory as a parameter-dependent polynomial system for the control switching sequence. A deflated Newton's method is then applied to exhaust all the real roots of the polynomial system. The root-finding procedure is informed by the Hermite quadratic form, which provides a sharp estimate on the number of real roots to be found. In the second part of the paper, the polynomial systems are sampled and solved to generate a synthetic dataset for the construction of a time-optimal deep neural network -- interpreted as a binary classifier -- via supervised learning. Numerical tests in integrators of increasing dimension assess the accuracy, robustness, and real-time-control capabilities of the approximate control law.
2503.17587
Hyun-Hwan Jeong
Jaeyeon Lee, Guantong Qi, Matthew Brady Neeley, Zhandong Liu, Hyun-Hwan Jeong
ConSol: Sequential Probability Ratio Testing to Find Consistent LLM Reasoning Paths Efficiently
null
null
null
null
cs.LG cs.AI
http://creativecommons.org/licenses/by/4.0/
Recent advancements in large language models (LLMs) integrating explicit reasoning, such as OpenAI's o3-mini, DeepSeek-R1, and QWQ-32B, enable smaller models to solve complex tasks by generating intermediate reasoning steps prior to providing answers. However, this approach significantly increases computational costs, both monetarily and environmentally. The widely-used self-consistency method further exacerbates these costs by aggregating multiple reasoning paths to improve accuracy, often requiring between 40 to 64 samples per task. Although aggregation effectively reduces variance and bias, additional sampling can lead to diminishing returns when early samples yield consistent results. To address inefficiencies, we propose leveraging Sequential Probability Ratio Testing (SPRT) to dynamically terminate sampling once sufficient consistency is achieved. We calibrate SPRT parameters specifically for LLM applications, accounting for sensitivity to detect the mode of the distribution. Our experiments demonstrate that incorporating SPRT significantly enhances token efficiency, achieving comparable accuracy to self-consistency methods but at a substantially reduced computational cost. To promote transparency and facilitate reproducibility, we have made the source code and datasets used in our experiments publicly available at our GitHub repository: https://github.com/LiuzLab/consol, or available as a PyPI package: pip install consol. We hope that this resource will support further research and encourage the development of new methods building upon our work.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 00:07:28 GMT" } ]
2025-03-25T00:00:00
[ [ "Lee", "Jaeyeon", "" ], [ "Qi", "Guantong", "" ], [ "Neeley", "Matthew Brady", "" ], [ "Liu", "Zhandong", "" ], [ "Jeong", "Hyun-Hwan", "" ] ]
TITLE: ConSol: Sequential Probability Ratio Testing to Find Consistent LLM Reasoning Paths Efficiently ABSTRACT: Recent advancements in large language models (LLMs) integrating explicit reasoning, such as OpenAI's o3-mini, DeepSeek-R1, and QWQ-32B, enable smaller models to solve complex tasks by generating intermediate reasoning steps prior to providing answers. However, this approach significantly increases computational costs, both monetarily and environmentally. The widely-used self-consistency method further exacerbates these costs by aggregating multiple reasoning paths to improve accuracy, often requiring between 40 to 64 samples per task. Although aggregation effectively reduces variance and bias, additional sampling can lead to diminishing returns when early samples yield consistent results. To address inefficiencies, we propose leveraging Sequential Probability Ratio Testing (SPRT) to dynamically terminate sampling once sufficient consistency is achieved. We calibrate SPRT parameters specifically for LLM applications, accounting for sensitivity to detect the mode of the distribution. Our experiments demonstrate that incorporating SPRT significantly enhances token efficiency, achieving comparable accuracy to self-consistency methods but at a substantially reduced computational cost. To promote transparency and facilitate reproducibility, we have made the source code and datasets used in our experiments publicly available at our GitHub repository: https://github.com/LiuzLab/consol, or available as a PyPI package: pip install consol. We hope that this resource will support further research and encourage the development of new methods building upon our work.
2503.17592
Alhasan Abdellatif
Alhasan Abdellatif, Hannah P. Menke, Julien Maes, Ahmed H. Elsheikh and Florian Doster
Benchmark Dataset for Pore-Scale CO2-Water Interaction
null
null
null
null
physics.chem-ph cs.LG physics.comp-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications, including carbon capture and storage (CCS). We introduce a comprehensive dataset generated from high-fidelity numerical simulations to capture the intricate interaction between CO2 and water at the pore scale. The dataset consists of 624 2D samples, each of size 512x512 with a resolution of 35 {\mu}m, covering 100 time steps under a constant CO2 injection rate. It includes various levels of heterogeneity, represented by different grain sizes with random variation in spacing, offering a robust testbed for developing predictive models. This dataset provides high-resolution temporal and spatial information crucial for benchmarking machine learning models.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 00:42:42 GMT" } ]
2025-03-25T00:00:00
[ [ "Abdellatif", "Alhasan", "" ], [ "Menke", "Hannah P.", "" ], [ "Maes", "Julien", "" ], [ "Elsheikh", "Ahmed H.", "" ], [ "Doster", "Florian", "" ] ]
TITLE: Benchmark Dataset for Pore-Scale CO2-Water Interaction ABSTRACT: Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications, including carbon capture and storage (CCS). We introduce a comprehensive dataset generated from high-fidelity numerical simulations to capture the intricate interaction between CO2 and water at the pore scale. The dataset consists of 624 2D samples, each of size 512x512 with a resolution of 35 {\mu}m, covering 100 time steps under a constant CO2 injection rate. It includes various levels of heterogeneity, represented by different grain sizes with random variation in spacing, offering a robust testbed for developing predictive models. This dataset provides high-resolution temporal and spatial information crucial for benchmarking machine learning models.
2503.17630
Bin Duan
Bin Duan, Matthew B.Dwyer, Guowei Yang
Generating Realistic, Diverse, and Fault-Revealing Inputs with Latent Space Interpolation for Testing Deep Neural Networks
null
null
null
null
cs.LG cs.SE
http://creativecommons.org/licenses/by/4.0/
Deep Neural Networks (DNNs) have been widely employed across various domains, including safety-critical systems, necessitating comprehensive testing to ensure their reliability. Although numerous DNN model testing methods have been proposed to generate adversarial samples that are capable of revealing faults, existing methods typically perturb samples in the input space and then mutate these based on feedback from the DNN model. These methods often result in test samples that are not realistic and with low-probability reveal faults. To address these limitations, we propose a black-box DNN test input generation method, ARGUS, to generate realistic, diverse, and fault-revealing test inputs. ARGUS first compresses samples into a continuous latent space and then perturbs the original samples by interpolating these with samples of different classes. Subsequently, we employ a vector quantizer and decoder to reconstruct adversarial samples back into the input space. Additionally, we employ discriminators both in the latent space and in the input space to ensure the realism of the generated samples. Evaluation of ARGUS in comparison with state-of-the-art black-box testing and white-box testing methods, shows that ARGUS excels in generating realistic and diverse adversarial samples relative to the target dataset, and ARGUS successfully perturbs all original samples and achieves up to 4 times higher error rate than the best baseline method. Furthermore, using these adversarial samples for model retraining can improve model classification accuracy.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 03:19:55 GMT" } ]
2025-03-25T00:00:00
[ [ "Duan", "Bin", "" ], [ "Dwyer", "Matthew B.", "" ], [ "Yang", "Guowei", "" ] ]
TITLE: Generating Realistic, Diverse, and Fault-Revealing Inputs with Latent Space Interpolation for Testing Deep Neural Networks ABSTRACT: Deep Neural Networks (DNNs) have been widely employed across various domains, including safety-critical systems, necessitating comprehensive testing to ensure their reliability. Although numerous DNN model testing methods have been proposed to generate adversarial samples that are capable of revealing faults, existing methods typically perturb samples in the input space and then mutate these based on feedback from the DNN model. These methods often result in test samples that are not realistic and with low-probability reveal faults. To address these limitations, we propose a black-box DNN test input generation method, ARGUS, to generate realistic, diverse, and fault-revealing test inputs. ARGUS first compresses samples into a continuous latent space and then perturbs the original samples by interpolating these with samples of different classes. Subsequently, we employ a vector quantizer and decoder to reconstruct adversarial samples back into the input space. Additionally, we employ discriminators both in the latent space and in the input space to ensure the realism of the generated samples. Evaluation of ARGUS in comparison with state-of-the-art black-box testing and white-box testing methods, shows that ARGUS excels in generating realistic and diverse adversarial samples relative to the target dataset, and ARGUS successfully perturbs all original samples and achieves up to 4 times higher error rate than the best baseline method. Furthermore, using these adversarial samples for model retraining can improve model classification accuracy.
2503.17632
Jiali Cheng
Jiali Cheng, Hadi Amiri
FairFlow: Mitigating Dataset Biases through Undecided Learning
EMNLP 2024
EMNLP 2024
null
null
cs.LG cs.AI cs.CL
http://creativecommons.org/licenses/by-nc-sa/4.0/
Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called ``FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases. The framework introduces two key components: a suite of data and model perturbation operations that generate different biased views of input samples, and a contrastive objective that learns debiased and robust representations from the resulting biased views of samples. Experiments show that FairFlow outperforms existing debiasing methods, particularly against out-of-domain and hard test samples without compromising the in-domain performance
[ { "version": "v1", "created": "Sat, 22 Mar 2025 03:35:51 GMT" } ]
2025-03-25T00:00:00
[ [ "Cheng", "Jiali", "" ], [ "Amiri", "Hadi", "" ] ]
TITLE: FairFlow: Mitigating Dataset Biases through Undecided Learning ABSTRACT: Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called ``FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases. The framework introduces two key components: a suite of data and model perturbation operations that generate different biased views of input samples, and a contrastive objective that learns debiased and robust representations from the resulting biased views of samples. Experiments show that FairFlow outperforms existing debiasing methods, particularly against out-of-domain and hard test samples without compromising the in-domain performance
2503.17633
Tejas Panambur
Tejas Panambur, Mario Parente
Enhancing Martian Terrain Recognition with Deep Constrained Clustering
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Martian terrain recognition is pivotal for advancing our understanding of topography, geomorphology, paleoclimate, and habitability. While deep clustering methods have shown promise in learning semantically homogeneous feature embeddings from Martian rover imagery, the natural variations in intensity, scale, and rotation pose significant challenges for accurate terrain classification. To address these limitations, we propose Deep Constrained Clustering with Metric Learning (DCCML), a novel algorithm that leverages multiple constraint types to guide the clustering process. DCCML incorporates soft must-link constraints derived from spatial and depth similarities between neighboring patches, alongside hard constraints from stereo camera pairs and temporally adjacent images. Experimental evaluation on the Curiosity rover dataset (with 150 clusters) demonstrates that DCCML increases homogeneous clusters by 16.7 percent while reducing the Davies-Bouldin Index from 3.86 to 1.82 and boosting retrieval accuracy from 86.71 percent to 89.86 percent. This improvement enables more precise classification of Martian geological features, advancing our capacity to analyze and understand the planet's landscape.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 03:38:16 GMT" } ]
2025-03-25T00:00:00
[ [ "Panambur", "Tejas", "" ], [ "Parente", "Mario", "" ] ]
TITLE: Enhancing Martian Terrain Recognition with Deep Constrained Clustering ABSTRACT: Martian terrain recognition is pivotal for advancing our understanding of topography, geomorphology, paleoclimate, and habitability. While deep clustering methods have shown promise in learning semantically homogeneous feature embeddings from Martian rover imagery, the natural variations in intensity, scale, and rotation pose significant challenges for accurate terrain classification. To address these limitations, we propose Deep Constrained Clustering with Metric Learning (DCCML), a novel algorithm that leverages multiple constraint types to guide the clustering process. DCCML incorporates soft must-link constraints derived from spatial and depth similarities between neighboring patches, alongside hard constraints from stereo camera pairs and temporally adjacent images. Experimental evaluation on the Curiosity rover dataset (with 150 clusters) demonstrates that DCCML increases homogeneous clusters by 16.7 percent while reducing the Davies-Bouldin Index from 3.86 to 1.82 and boosting retrieval accuracy from 86.71 percent to 89.86 percent. This improvement enables more precise classification of Martian geological features, advancing our capacity to analyze and understand the planet's landscape.
2503.17641
Chi Zhang
Chi Zhang, Chengjian Feng, Feng Yan, Qiming Zhang, Mingjin Zhang, Yujie Zhong, Jing Zhang, Lin Ma
InstructVEdit: A Holistic Approach for Instructional Video Editing
https://o937-blip.github.io/InstructVEdit
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data. This scarcity not only limits the availability of training data but also hinders the systematic exploration of model architectures and training strategies. While prior work has improved specific aspects of video editing (e.g., synthesizing a video dataset using image editing techniques or decomposed video editing training), a holistic framework addressing the above challenges remains underexplored. In this study, we introduce InstructVEdit, a full-cycle instructional video editing approach that: (1) establishes a reliable dataset curation workflow to initialize training, (2) incorporates two model architectural improvements to enhance edit quality while preserving temporal consistency, and (3) proposes an iterative refinement strategy leveraging real-world data to enhance generalization and minimize train-test discrepancies. Extensive experiments show that InstructVEdit achieves state-of-the-art performance in instruction-based video editing, demonstrating robust adaptability to diverse real-world scenarios. Project page: https://o937-blip.github.io/InstructVEdit.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 04:12:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhang", "Chi", "" ], [ "Feng", "Chengjian", "" ], [ "Yan", "Feng", "" ], [ "Zhang", "Qiming", "" ], [ "Zhang", "Mingjin", "" ], [ "Zhong", "Yujie", "" ], [ "Zhang", "Jing", "" ], [ "Ma", "Lin", "" ] ]
TITLE: InstructVEdit: A Holistic Approach for Instructional Video Editing ABSTRACT: Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data. This scarcity not only limits the availability of training data but also hinders the systematic exploration of model architectures and training strategies. While prior work has improved specific aspects of video editing (e.g., synthesizing a video dataset using image editing techniques or decomposed video editing training), a holistic framework addressing the above challenges remains underexplored. In this study, we introduce InstructVEdit, a full-cycle instructional video editing approach that: (1) establishes a reliable dataset curation workflow to initialize training, (2) incorporates two model architectural improvements to enhance edit quality while preserving temporal consistency, and (3) proposes an iterative refinement strategy leveraging real-world data to enhance generalization and minimize train-test discrepancies. Extensive experiments show that InstructVEdit achieves state-of-the-art performance in instruction-based video editing, demonstrating robust adaptability to diverse real-world scenarios. Project page: https://o937-blip.github.io/InstructVEdit.
2503.17645
Adam Atanas
Adam Atanas, Kai Liu
A Modular Dataset to Demonstrate LLM Abstraction Capability
7 pages, 5 figures. Submitted to ACL 2025
null
null
null
cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Large language models (LLMs) exhibit impressive capabilities but struggle with reasoning errors due to hallucinations and flawed logic. To investigate their internal representations of reasoning, we introduce ArrangementPuzzle, a novel puzzle dataset with structured solutions and automated stepwise correctness verification. We trained a classifier model on LLM activations on this dataset and found that it achieved over 80% accuracy in predicting reasoning correctness, implying that LLMs internally distinguish between correct and incorrect reasoning steps, with the strongest representations in middle-late Transformer layers. Further analysis reveals that LLMs encode abstract reasoning concepts within the middle activation layers of the transformer architecture, distinguishing logical from semantic equivalence. These findings provide insights into LLM reasoning mechanisms and contribute to improving AI reliability and interpretability, thereby offering the possibility to manipulate and refine LLM reasoning.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 04:25:30 GMT" } ]
2025-03-25T00:00:00
[ [ "Atanas", "Adam", "" ], [ "Liu", "Kai", "" ] ]
TITLE: A Modular Dataset to Demonstrate LLM Abstraction Capability ABSTRACT: Large language models (LLMs) exhibit impressive capabilities but struggle with reasoning errors due to hallucinations and flawed logic. To investigate their internal representations of reasoning, we introduce ArrangementPuzzle, a novel puzzle dataset with structured solutions and automated stepwise correctness verification. We trained a classifier model on LLM activations on this dataset and found that it achieved over 80% accuracy in predicting reasoning correctness, implying that LLMs internally distinguish between correct and incorrect reasoning steps, with the strongest representations in middle-late Transformer layers. Further analysis reveals that LLMs encode abstract reasoning concepts within the middle activation layers of the transformer architecture, distinguishing logical from semantic equivalence. These findings provide insights into LLM reasoning mechanisms and contribute to improving AI reliability and interpretability, thereby offering the possibility to manipulate and refine LLM reasoning.
2503.17646
Yen-Cheng Chang
Yen Cheng Chang, Jesse Codling, Yiwen Dong, Jiale Zhang, Jiasi Chen, Hae Young Noh, and Pei Zhang
Leveraging Audio Representations for Vibration-Based Crowd Monitoring in Stadiums
null
null
null
null
cs.SD cs.CV
http://creativecommons.org/licenses/by/4.0/
Crowd monitoring in sports stadiums is important to enhance public safety and improve the audience experience. Existing approaches mainly rely on cameras and microphones, which can cause significant disturbances and often raise privacy concerns. In this paper, we sense floor vibration, which provides a less disruptive and more non-intrusive way of crowd sensing, to predict crowd behavior. However, since the vibration-based crowd monitoring approach is newly developed, one main challenge is the lack of training data due to sports stadiums being large public spaces with complex physical activities. In this paper, we present ViLA (Vibration Leverage Audio), a vibration-based method that reduces the dependency on labeled data by pre-training with unlabeled cross-modality data. ViLA is first pre-trained on audio data in an unsupervised manner and then fine-tuned with a minimal amount of in-domain vibration data. By leveraging publicly available audio datasets, ViLA learns the wave behaviors from audio and then adapts the representation to vibration, reducing the reliance on domain-specific vibration data. Our real-world experiments demonstrate that pre-training the vibration model using publicly available audio data (YouTube8M) achieved up to a 5.8x error reduction compared to the model without audio pre-training.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 04:27:30 GMT" } ]
2025-03-25T00:00:00
[ [ "Chang", "Yen Cheng", "" ], [ "Codling", "Jesse", "" ], [ "Dong", "Yiwen", "" ], [ "Zhang", "Jiale", "" ], [ "Chen", "Jiasi", "" ], [ "Noh", "Hae Young", "" ], [ "Zhang", "Pei", "" ] ]
TITLE: Leveraging Audio Representations for Vibration-Based Crowd Monitoring in Stadiums ABSTRACT: Crowd monitoring in sports stadiums is important to enhance public safety and improve the audience experience. Existing approaches mainly rely on cameras and microphones, which can cause significant disturbances and often raise privacy concerns. In this paper, we sense floor vibration, which provides a less disruptive and more non-intrusive way of crowd sensing, to predict crowd behavior. However, since the vibration-based crowd monitoring approach is newly developed, one main challenge is the lack of training data due to sports stadiums being large public spaces with complex physical activities. In this paper, we present ViLA (Vibration Leverage Audio), a vibration-based method that reduces the dependency on labeled data by pre-training with unlabeled cross-modality data. ViLA is first pre-trained on audio data in an unsupervised manner and then fine-tuned with a minimal amount of in-domain vibration data. By leveraging publicly available audio datasets, ViLA learns the wave behaviors from audio and then adapts the representation to vibration, reducing the reliance on domain-specific vibration data. Our real-world experiments demonstrate that pre-training the vibration model using publicly available audio data (YouTube8M) achieved up to a 5.8x error reduction compared to the model without audio pre-training.
2503.17650
Xi Xiao
Xi Xiao, Yunbei Zhang, Yanshuh Li, Xingjian Li, Tianyang Wang, Jihun Hamm, Xiao Wang, Min Xu
Visual Variational Autoencoder Prompt Tuning
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of full fine-tuning. While existing visual prompt tuning (VPT) methods have made significant strides, they predominantly rely on static, domain-specific prompts that fail to capture the rich visual diversity within individual instances. This paper introduces V$^2$APT (Visual Variational Autoencoder Prompt Tuning), a novel framework that generates dynamic, input-dependent prompts using a variational autoencoder architecture. By learning a latent representation of image-specific features and decoding them into customized prompts, V$^2$APT adapts to the unique visual characteristics of each input. Extensive experiments on FGVC, HTA, and VTAB-1k benchmarks demonstrate that our approach consistently outperforms state-of-the-art PEFT methods. Notably, V$^2$APT achieves +3.2\% improvement over VPT-Deep on HTA, with an average performance gain of +2.0\% across all three datasets.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 04:59:51 GMT" } ]
2025-03-25T00:00:00
[ [ "Xiao", "Xi", "" ], [ "Zhang", "Yunbei", "" ], [ "Li", "Yanshuh", "" ], [ "Li", "Xingjian", "" ], [ "Wang", "Tianyang", "" ], [ "Hamm", "Jihun", "" ], [ "Wang", "Xiao", "" ], [ "Xu", "Min", "" ] ]
TITLE: Visual Variational Autoencoder Prompt Tuning ABSTRACT: Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of full fine-tuning. While existing visual prompt tuning (VPT) methods have made significant strides, they predominantly rely on static, domain-specific prompts that fail to capture the rich visual diversity within individual instances. This paper introduces V$^2$APT (Visual Variational Autoencoder Prompt Tuning), a novel framework that generates dynamic, input-dependent prompts using a variational autoencoder architecture. By learning a latent representation of image-specific features and decoding them into customized prompts, V$^2$APT adapts to the unique visual characteristics of each input. Extensive experiments on FGVC, HTA, and VTAB-1k benchmarks demonstrate that our approach consistently outperforms state-of-the-art PEFT methods. Notably, V$^2$APT achieves +3.2\% improvement over VPT-Deep on HTA, with an average performance gain of +2.0\% across all three datasets.
2503.17660
Miao Zhang
Kun Li, Jianhui Wang, Miao Zhang, Xueqian Wang
OMR-Diffusion:Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Intent Understanding
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Generative AI has significantly advanced text-driven image generation, but it still faces challenges in producing outputs that consistently align with evolving user preferences and intents, particularly in multi-turn dialogue scenarios. In this research, We present a Visual Co-Adaptation (VCA) framework that incorporates human-in-the-loop feedback, utilizing a well-trained reward model specifically designed to closely align with human preferences. Using a diverse multi-turn dialogue dataset, the framework applies multiple reward functions (such as diversity, consistency, and preference feedback) to refine the diffusion model through LoRA, effectively optimizing image generation based on user input. We also constructed multi-round dialogue datasets with prompts and image pairs that well-fit user intent. Experiments show the model achieves 508 wins in human evaluation, outperforming DALL-E 3 (463 wins) and others. It also achieves 3.4 rounds in dialogue efficiency (vs. 13.7 for DALL-E 3) and excels in metrics like LPIPS (0.15) and BLIP (0.59). Various experiments demonstrate the effectiveness of the proposed method over state-of-the-art baselines, with significant improvements in image consistency and alignment with user intent.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 06:10:57 GMT" } ]
2025-03-25T00:00:00
[ [ "Li", "Kun", "" ], [ "Wang", "Jianhui", "" ], [ "Zhang", "Miao", "" ], [ "Wang", "Xueqian", "" ] ]
TITLE: OMR-Diffusion:Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Intent Understanding ABSTRACT: Generative AI has significantly advanced text-driven image generation, but it still faces challenges in producing outputs that consistently align with evolving user preferences and intents, particularly in multi-turn dialogue scenarios. In this research, We present a Visual Co-Adaptation (VCA) framework that incorporates human-in-the-loop feedback, utilizing a well-trained reward model specifically designed to closely align with human preferences. Using a diverse multi-turn dialogue dataset, the framework applies multiple reward functions (such as diversity, consistency, and preference feedback) to refine the diffusion model through LoRA, effectively optimizing image generation based on user input. We also constructed multi-round dialogue datasets with prompts and image pairs that well-fit user intent. Experiments show the model achieves 508 wins in human evaluation, outperforming DALL-E 3 (463 wins) and others. It also achieves 3.4 rounds in dialogue efficiency (vs. 13.7 for DALL-E 3) and excels in metrics like LPIPS (0.15) and BLIP (0.59). Various experiments demonstrate the effectiveness of the proposed method over state-of-the-art baselines, with significant improvements in image consistency and alignment with user intent.
2503.17664
Md. Shaheenur Islam Sumon
Md. Shaheenur Islam Sumon, Md. Sakib Bin Islam, Md. Sohanur Rahman, Md. Sakib Abrar Hossain, Amith Khandakar, Anwarul Hasan, M Murugappan, Muhammad E. H. Chowdhury
CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data
This paper is currently under review in the Health Information Science and Systems journal
null
null
null
cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-of-the-art models to evaluate our transformer-driven framework.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 06:17:08 GMT" } ]
2025-03-25T00:00:00
[ [ "Sumon", "Md. Shaheenur Islam", "" ], [ "Islam", "Md. Sakib Bin", "" ], [ "Rahman", "Md. Sohanur", "" ], [ "Hossain", "Md. Sakib Abrar", "" ], [ "Khandakar", "Amith", "" ], [ "Hasan", "Anwarul", "" ], [ "Murugappan", "M", "" ], [ "Chowdhury", "Muhammad E. H.", "" ] ]
TITLE: CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data ABSTRACT: The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-of-the-art models to evaluate our transformer-driven framework.
2503.17666
Peijin Guo
Peijin Guo, Minghui Li, Hewen Pan, Ruixiang Huang, Lulu Xue, Shengqing Hu, Zikang Guo, Wei Wan, Shengshan Hu
Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction
2025 IEEE International Conference on Multimedia and Expo (ICME 2025), June 30 - July 4, 2025, Nantes, France
null
null
null
cs.LG q-bio.QM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 06:23:51 GMT" } ]
2025-03-25T00:00:00
[ [ "Guo", "Peijin", "" ], [ "Li", "Minghui", "" ], [ "Pan", "Hewen", "" ], [ "Huang", "Ruixiang", "" ], [ "Xue", "Lulu", "" ], [ "Hu", "Shengqing", "" ], [ "Guo", "Zikang", "" ], [ "Wan", "Wei", "" ], [ "Hu", "Shengshan", "" ] ]
TITLE: Multi-Modality Representation Learning for Antibody-Antigen Interactions Prediction ABSTRACT: While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.
2503.17671
OuCheng Huang
Oucheng Huang, Yuhang Ma, Zeng Zhao, Mingrui Wu, Jiayi Ji, Rongsheng Zhang, Zhipeng Hu, Xiaoshuai Sun and Rongrong Ji
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation
null
null
null
null
cs.MA cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
ComfyUI provides a widely-adopted, workflow-based interface that enables users to customize various image generation tasks through an intuitive node-based architecture. However, the intricate connections between nodes and diverse modules often present a steep learning curve for users. In this paper, we introduce ComfyGPT, the first self-optimizing multi-agent system designed to generate ComfyUI workflows based on task descriptions automatically. ComfyGPT comprises four specialized agents: ReformatAgent, FlowAgent, RefineAgent, and ExecuteAgent. The core innovation of ComfyGPT lies in two key aspects. First, it focuses on generating individual node links rather than entire workflows, significantly improving generation precision. Second, we proposed FlowAgent, a LLM-based workflow generation agent that uses both supervised fine-tuning (SFT) and reinforcement learning (RL) to improve workflow generation accuracy. Moreover, we introduce FlowDataset, a large-scale dataset containing 13,571 workflow-description pairs, and FlowBench, a comprehensive benchmark for evaluating workflow generation systems. We also propose four novel evaluation metrics: Format Validation (FV), Pass Accuracy (PA), Pass Instruct Alignment (PIA), and Pass Node Diversity (PND). Experimental results demonstrate that ComfyGPT significantly outperforms existing LLM-based methods in workflow generation.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 06:48:50 GMT" } ]
2025-03-25T00:00:00
[ [ "Huang", "Oucheng", "" ], [ "Ma", "Yuhang", "" ], [ "Zhao", "Zeng", "" ], [ "Wu", "Mingrui", "" ], [ "Ji", "Jiayi", "" ], [ "Zhang", "Rongsheng", "" ], [ "Hu", "Zhipeng", "" ], [ "Sun", "Xiaoshuai", "" ], [ "Ji", "Rongrong", "" ] ]
TITLE: ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation ABSTRACT: ComfyUI provides a widely-adopted, workflow-based interface that enables users to customize various image generation tasks through an intuitive node-based architecture. However, the intricate connections between nodes and diverse modules often present a steep learning curve for users. In this paper, we introduce ComfyGPT, the first self-optimizing multi-agent system designed to generate ComfyUI workflows based on task descriptions automatically. ComfyGPT comprises four specialized agents: ReformatAgent, FlowAgent, RefineAgent, and ExecuteAgent. The core innovation of ComfyGPT lies in two key aspects. First, it focuses on generating individual node links rather than entire workflows, significantly improving generation precision. Second, we proposed FlowAgent, a LLM-based workflow generation agent that uses both supervised fine-tuning (SFT) and reinforcement learning (RL) to improve workflow generation accuracy. Moreover, we introduce FlowDataset, a large-scale dataset containing 13,571 workflow-description pairs, and FlowBench, a comprehensive benchmark for evaluating workflow generation systems. We also propose four novel evaluation metrics: Format Validation (FV), Pass Accuracy (PA), Pass Instruct Alignment (PIA), and Pass Node Diversity (PND). Experimental results demonstrate that ComfyGPT significantly outperforms existing LLM-based methods in workflow generation.
2503.17672
Qing Zhong
Qing Zhong, Peng-Tao Jiang, Wen Wang, Guodong Ding, Lin Wu, Kaiqi Huang
A Temporal Modeling Framework for Video Pre-Training on Video Instance Segmentation
7 pages, 5figures, 6 tables, Accepted to ICME 2025
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Contemporary Video Instance Segmentation (VIS) methods typically adhere to a pre-train then fine-tune regime, where a segmentation model trained on images is fine-tuned on videos. However, the lack of temporal knowledge in the pre-trained model introduces a domain gap which may adversely affect the VIS performance. To effectively bridge this gap, we present a novel video pre-training approach to enhance VIS models, especially for videos with intricate instance relationships. Our crucial innovation focuses on reducing disparities between the pre-training and fine-tuning stages. Specifically, we first introduce consistent pseudo-video augmentations to create diverse pseudo-video samples for pre-training while maintaining the instance consistency across frames. Then, we incorporate a multi-scale temporal module to enhance the model's ability to model temporal relations through self- and cross-attention at short- and long-term temporal spans. Our approach does not set constraints on model architecture and can integrate seamlessly with various VIS methods. Experiment results on commonly adopted VIS benchmarks show that our method consistently outperforms state-of-the-art methods. Our approach achieves a notable 4.0% increase in average precision on the challenging OVIS dataset.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 07:01:25 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhong", "Qing", "" ], [ "Jiang", "Peng-Tao", "" ], [ "Wang", "Wen", "" ], [ "Ding", "Guodong", "" ], [ "Wu", "Lin", "" ], [ "Huang", "Kaiqi", "" ] ]
TITLE: A Temporal Modeling Framework for Video Pre-Training on Video Instance Segmentation ABSTRACT: Contemporary Video Instance Segmentation (VIS) methods typically adhere to a pre-train then fine-tune regime, where a segmentation model trained on images is fine-tuned on videos. However, the lack of temporal knowledge in the pre-trained model introduces a domain gap which may adversely affect the VIS performance. To effectively bridge this gap, we present a novel video pre-training approach to enhance VIS models, especially for videos with intricate instance relationships. Our crucial innovation focuses on reducing disparities between the pre-training and fine-tuning stages. Specifically, we first introduce consistent pseudo-video augmentations to create diverse pseudo-video samples for pre-training while maintaining the instance consistency across frames. Then, we incorporate a multi-scale temporal module to enhance the model's ability to model temporal relations through self- and cross-attention at short- and long-term temporal spans. Our approach does not set constraints on model architecture and can integrate seamlessly with various VIS methods. Experiment results on commonly adopted VIS benchmarks show that our method consistently outperforms state-of-the-art methods. Our approach achieves a notable 4.0% increase in average precision on the challenging OVIS dataset.
2503.17677
Huitong Chen
Huitong Chen, Yu Wang, Yan Fan, Guosong Jiang, Qinghua Hu
Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds
Accepted to CVPR 2025
null
null
null
cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Class incremental learning (CIL) aims to enable models to continuously learn new classes without catastrophically forgetting old ones. A promising direction is to learn and use prototypes of classes during incremental updates. Despite simplicity and intuition, we find that such methods suffer from inadequate representation capability and unsatisfied feature overlap. These two factors cause class-wise confusion and limited performance. In this paper, we develop a Confusion-REduced AuTo-Encoder classifier (CREATE) for CIL. Specifically, our method employs a lightweight auto-encoder module to learn compact manifold for each class in the latent subspace, constraining samples to be well reconstructed only on the semantically correct auto-encoder. Thus, the representation stability and capability of class distributions are enhanced, alleviating the potential class-wise confusion problem. To further distinguish the overlapped features, we propose a confusion-aware latent space separation loss that ensures samples are closely distributed in their corresponding low-dimensional manifold while keeping away from the distributions of features from other classes. Our method demonstrates stronger representational capacity and discrimination ability by learning disentangled manifolds and reduces class confusion. Extensive experiments on multiple datasets and settings show that CREATE outperforms other state-of-the-art methods up to 5.41%.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 07:07:15 GMT" } ]
2025-03-25T00:00:00
[ [ "Chen", "Huitong", "" ], [ "Wang", "Yu", "" ], [ "Fan", "Yan", "" ], [ "Jiang", "Guosong", "" ], [ "Hu", "Qinghua", "" ] ]
TITLE: Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds ABSTRACT: Class incremental learning (CIL) aims to enable models to continuously learn new classes without catastrophically forgetting old ones. A promising direction is to learn and use prototypes of classes during incremental updates. Despite simplicity and intuition, we find that such methods suffer from inadequate representation capability and unsatisfied feature overlap. These two factors cause class-wise confusion and limited performance. In this paper, we develop a Confusion-REduced AuTo-Encoder classifier (CREATE) for CIL. Specifically, our method employs a lightweight auto-encoder module to learn compact manifold for each class in the latent subspace, constraining samples to be well reconstructed only on the semantically correct auto-encoder. Thus, the representation stability and capability of class distributions are enhanced, alleviating the potential class-wise confusion problem. To further distinguish the overlapped features, we propose a confusion-aware latent space separation loss that ensures samples are closely distributed in their corresponding low-dimensional manifold while keeping away from the distributions of features from other classes. Our method demonstrates stronger representational capacity and discrimination ability by learning disentangled manifolds and reduces class confusion. Extensive experiments on multiple datasets and settings show that CREATE outperforms other state-of-the-art methods up to 5.41%.
2503.17682
Jiaming Ji
Jiaming Ji, Xinyu Chen, Rui Pan, Han Zhu, Conghui Zhang, Jiahao Li, Donghai Hong, Boyuan Chen, Jiayi Zhou, Kaile Wang, Juntao Dai, Chi-Min Chan, Sirui Han, Yike Guo, Yaodong Yang
Safe RLHF-V: Safe Reinforcement Learning from Human Feedback in Multimodal Large Language Models
null
null
null
null
cs.LG cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multimodal large language models (MLLMs) are critical for developing general-purpose AI assistants, yet they face growing safety risks. How can we ensure that MLLMs are safely aligned to prevent undesired behaviors such as discrimination, misinformation, or violations of ethical standards? In a further step, we need to explore how to fine-tune MLLMs to enhance reasoning performance while ensuring they satisfy safety constraints. Fundamentally, this can be formulated as a min-max optimization problem. In this study, we propose Safe RLHF-V, the first multimodal safety alignment framework that jointly optimizes helpfulness and safety using separate multimodal reward and cost models within a Lagrangian-based constrained optimization framework. Given that there is a lack of preference datasets that separate helpfulness and safety in multimodal scenarios, we introduce BeaverTails-V, the first open-source dataset with dual preference annotations for helpfulness and safety, along with multi-level safety labels (minor, moderate, severe). Additionally, we design a Multi-level Guardrail System to proactively defend against unsafe queries and adversarial attacks. By applying the Beaver-Guard-V moderation for 5 rounds of filtering and re-generation on the precursor model, the overall safety of the upstream model is significantly improved by an average of 40.9%. Experimental results demonstrate that fine-tuning different MLLMs with Safe RLHF can effectively enhance model helpfulness while ensuring improved safety. Specifically, Safe RLHF-V improves model safety by 34.2% and helpfulness by 34.3%. All of datasets, models, and code can be found at https://github.com/SafeRLHF-V to support the safety development of MLLMs and reduce potential societal risks.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 07:40:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Ji", "Jiaming", "" ], [ "Chen", "Xinyu", "" ], [ "Pan", "Rui", "" ], [ "Zhu", "Han", "" ], [ "Zhang", "Conghui", "" ], [ "Li", "Jiahao", "" ], [ "Hong", "Donghai", "" ], [ "Chen", "Boyuan", "" ], [ "Zhou", "Jiayi", "" ], [ "Wang", "Kaile", "" ], [ "Dai", "Juntao", "" ], [ "Chan", "Chi-Min", "" ], [ "Han", "Sirui", "" ], [ "Guo", "Yike", "" ], [ "Yang", "Yaodong", "" ] ]
TITLE: Safe RLHF-V: Safe Reinforcement Learning from Human Feedback in Multimodal Large Language Models ABSTRACT: Multimodal large language models (MLLMs) are critical for developing general-purpose AI assistants, yet they face growing safety risks. How can we ensure that MLLMs are safely aligned to prevent undesired behaviors such as discrimination, misinformation, or violations of ethical standards? In a further step, we need to explore how to fine-tune MLLMs to enhance reasoning performance while ensuring they satisfy safety constraints. Fundamentally, this can be formulated as a min-max optimization problem. In this study, we propose Safe RLHF-V, the first multimodal safety alignment framework that jointly optimizes helpfulness and safety using separate multimodal reward and cost models within a Lagrangian-based constrained optimization framework. Given that there is a lack of preference datasets that separate helpfulness and safety in multimodal scenarios, we introduce BeaverTails-V, the first open-source dataset with dual preference annotations for helpfulness and safety, along with multi-level safety labels (minor, moderate, severe). Additionally, we design a Multi-level Guardrail System to proactively defend against unsafe queries and adversarial attacks. By applying the Beaver-Guard-V moderation for 5 rounds of filtering and re-generation on the precursor model, the overall safety of the upstream model is significantly improved by an average of 40.9%. Experimental results demonstrate that fine-tuning different MLLMs with Safe RLHF can effectively enhance model helpfulness while ensuring improved safety. Specifically, Safe RLHF-V improves model safety by 34.2% and helpfulness by 34.3%. All of datasets, models, and code can be found at https://github.com/SafeRLHF-V to support the safety development of MLLMs and reduce potential societal risks.
2503.17683
Eduardo Fernandes Montesuma
Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngol\`e Mboula
Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
Accepted at ICASSP 2025
null
null
null
cs.LG stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Decentralized Multi-Source Domain Adaptation (DMSDA) is a challenging task that aims to transfer knowledge from multiple related and heterogeneous source domains to an unlabeled target domain within a decentralized framework. Our work tackles DMSDA through a fully decentralized federated approach. In particular, we extend the Federated Dataset Dictionary Learning (FedDaDiL) framework by eliminating the necessity for a central server. FedDaDiL leverages Wasserstein barycenters to model the distributional shift across multiple clients, enabling effective adaptation while preserving data privacy. By decentralizing this framework, we enhance its robustness, scalability, and privacy, removing the risk of a single point of failure. We compare our method to its federated counterpart and other benchmark algorithms, showing that our approach effectively adapts source domains to an unlabeled target domain in a fully decentralized manner.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 07:48:48 GMT" } ]
2025-03-25T00:00:00
[ [ "Clain", "Rebecca", "" ], [ "Montesuma", "Eduardo Fernandes", "" ], [ "Mboula", "Fred Ngolè", "" ] ]
TITLE: Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation ABSTRACT: Decentralized Multi-Source Domain Adaptation (DMSDA) is a challenging task that aims to transfer knowledge from multiple related and heterogeneous source domains to an unlabeled target domain within a decentralized framework. Our work tackles DMSDA through a fully decentralized federated approach. In particular, we extend the Federated Dataset Dictionary Learning (FedDaDiL) framework by eliminating the necessity for a central server. FedDaDiL leverages Wasserstein barycenters to model the distributional shift across multiple clients, enabling effective adaptation while preserving data privacy. By decentralizing this framework, we enhance its robustness, scalability, and privacy, removing the risk of a single point of failure. We compare our method to its federated counterpart and other benchmark algorithms, showing that our approach effectively adapts source domains to an unlabeled target domain in a fully decentralized manner.
2503.17697
Yanan Ma
Yanan Ma, Senkang Hu, Zhengru Fang, Yun Ji, Yiqin Deng, and Yuguang Fang
Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Autonomous Driving
16 pages, 5 figures
null
null
null
cs.RO cs.DC
http://creativecommons.org/licenses/by/4.0/
To accommodate constantly changing road conditions, real-time model training is essential for autonomous driving (AD). Federated learning (FL) serves as a promising paradigm to enable autonomous vehicles to train models collaboratively with their onboard computing resources. However, existing vehicle selection schemes for FL all assume predetermined and location-independent vehicles' datasets, neglecting the fact that vehicles collect training data along their routes, thereby resulting in suboptimal vehicle selection. To improve the perception quality in AD for a region, we propose Sense4FL, a vehicular crowdsensing-enhanced FL framework featuring trajectory-dependent vehicular training data collection. To this end, we first derive the convergence bound of FL by considering the impact of both vehicles' uncertain trajectories and uploading probabilities, from which we discover that minimizing the training loss is equivalent to minimizing a weighted sum of local and global earth mover's distance (EMD) between vehicles' collected data distribution and global data distribution. Based on this observation, we formulate the trajectory-dependent vehicle selection and data collection problem for FL in AD. Given that the problem is NP-hard, we develop an efficient algorithm to find the solution with an approximation guarantee. Extensive simulation results have demonstrated the effectiveness of our approach in improving object detection performance compared with existing benchmarks.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 08:39:01 GMT" } ]
2025-03-25T00:00:00
[ [ "Ma", "Yanan", "" ], [ "Hu", "Senkang", "" ], [ "Fang", "Zhengru", "" ], [ "Ji", "Yun", "" ], [ "Deng", "Yiqin", "" ], [ "Fang", "Yuguang", "" ] ]
TITLE: Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Autonomous Driving ABSTRACT: To accommodate constantly changing road conditions, real-time model training is essential for autonomous driving (AD). Federated learning (FL) serves as a promising paradigm to enable autonomous vehicles to train models collaboratively with their onboard computing resources. However, existing vehicle selection schemes for FL all assume predetermined and location-independent vehicles' datasets, neglecting the fact that vehicles collect training data along their routes, thereby resulting in suboptimal vehicle selection. To improve the perception quality in AD for a region, we propose Sense4FL, a vehicular crowdsensing-enhanced FL framework featuring trajectory-dependent vehicular training data collection. To this end, we first derive the convergence bound of FL by considering the impact of both vehicles' uncertain trajectories and uploading probabilities, from which we discover that minimizing the training loss is equivalent to minimizing a weighted sum of local and global earth mover's distance (EMD) between vehicles' collected data distribution and global data distribution. Based on this observation, we formulate the trajectory-dependent vehicle selection and data collection problem for FL in AD. Given that the problem is NP-hard, we develop an efficient algorithm to find the solution with an approximation guarantee. Extensive simulation results have demonstrated the effectiveness of our approach in improving object detection performance compared with existing benchmarks.
2503.17699
Haolin Qin
Haolin Qin, Tingfa Xu, Tianhao Li, Zhenxiang Chen, Tao Feng, Jianan Li
MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking
CVPR2025
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
UAV tracking faces significant challenges in real-world scenarios, such as small-size targets and occlusions, which limit the performance of RGB-based trackers. Multispectral images (MSI), which capture additional spectral information, offer a promising solution to these challenges. However, progress in this field has been hindered by the lack of relevant datasets. To address this gap, we introduce the first large-scale Multispectral UAV Single Object Tracking dataset (MUST), which includes 250 video sequences spanning diverse environments and challenges, providing a comprehensive data foundation for multispectral UAV tracking. We also propose a novel tracking framework, UNTrack, which encodes unified spectral, spatial, and temporal features from spectrum prompts, initial templates, and sequential searches. UNTrack employs an asymmetric transformer with a spectral background eliminate mechanism for optimal relationship modeling and an encoder that continuously updates the spectrum prompt to refine tracking, improving both accuracy and efficiency. Extensive experiments show that our proposed UNTrack outperforms state-of-the-art UAV trackers. We believe our dataset and framework will drive future research in this area. The dataset is available on https://github.com/q2479036243/MUST-Multispectral-UAV-Single-Object-Tracking.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 08:47:28 GMT" } ]
2025-03-25T00:00:00
[ [ "Qin", "Haolin", "" ], [ "Xu", "Tingfa", "" ], [ "Li", "Tianhao", "" ], [ "Chen", "Zhenxiang", "" ], [ "Feng", "Tao", "" ], [ "Li", "Jianan", "" ] ]
TITLE: MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking ABSTRACT: UAV tracking faces significant challenges in real-world scenarios, such as small-size targets and occlusions, which limit the performance of RGB-based trackers. Multispectral images (MSI), which capture additional spectral information, offer a promising solution to these challenges. However, progress in this field has been hindered by the lack of relevant datasets. To address this gap, we introduce the first large-scale Multispectral UAV Single Object Tracking dataset (MUST), which includes 250 video sequences spanning diverse environments and challenges, providing a comprehensive data foundation for multispectral UAV tracking. We also propose a novel tracking framework, UNTrack, which encodes unified spectral, spatial, and temporal features from spectrum prompts, initial templates, and sequential searches. UNTrack employs an asymmetric transformer with a spectral background eliminate mechanism for optimal relationship modeling and an encoder that continuously updates the spectrum prompt to refine tracking, improving both accuracy and efficiency. Extensive experiments show that our proposed UNTrack outperforms state-of-the-art UAV trackers. We believe our dataset and framework will drive future research in this area. The dataset is available on https://github.com/q2479036243/MUST-Multispectral-UAV-Single-Object-Tracking.
2503.17704
Liang Jiang
Liang Jiang, Yuzhou Cheng, Kun Luo, Jianren Fan
PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
null
null
null
null
physics.flu-dyn cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Physics-informed neural networks (PINNs) demonstrate promising potential in parameterized engineering turbulence optimization problems but face challenges, such as high data requirements and low computational accuracy when applied to engineering turbulence problems. This study proposes a framework that enhances the ability of PINNs to solve parametric turbulence problems without training datasets from experiments or CFD-Parametric Turbulence PINNs (PT-PINNs)). Two key methods are introduced to improve the accuracy and robustness of this framework. The first is a soft constraint method for turbulent viscosity calculation. The second is a pre-training method based on the conservation of flow rate in the flow field. The effectiveness of PT-PINNs is validated using a three-dimensional backward-facing step (BFS) turbulence problem with two varying parameters (Re = 3000-200000, ER = 1.1-1.5). PT-PINNs produce predictions that closely match experimental data and computational fluid dynamics (CFD) results across various conditions. Moreover, PT-PINNs offer a computational efficiency advantage over traditional CFD methods. The total time required to construct the parametric BFS turbulence model is 39 hours, one-sixteenth of the time required by traditional numerical methods. The inference time for a single-condition prediction is just 40 seconds-only 0.5% of a single CFD computation. These findings highlight the potential of PT-PINNs for future applications in engineering turbulence optimization problems.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 09:10:53 GMT" } ]
2025-03-25T00:00:00
[ [ "Jiang", "Liang", "" ], [ "Cheng", "Yuzhou", "" ], [ "Luo", "Kun", "" ], [ "Fan", "Jianren", "" ] ]
TITLE: PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks ABSTRACT: Physics-informed neural networks (PINNs) demonstrate promising potential in parameterized engineering turbulence optimization problems but face challenges, such as high data requirements and low computational accuracy when applied to engineering turbulence problems. This study proposes a framework that enhances the ability of PINNs to solve parametric turbulence problems without training datasets from experiments or CFD-Parametric Turbulence PINNs (PT-PINNs)). Two key methods are introduced to improve the accuracy and robustness of this framework. The first is a soft constraint method for turbulent viscosity calculation. The second is a pre-training method based on the conservation of flow rate in the flow field. The effectiveness of PT-PINNs is validated using a three-dimensional backward-facing step (BFS) turbulence problem with two varying parameters (Re = 3000-200000, ER = 1.1-1.5). PT-PINNs produce predictions that closely match experimental data and computational fluid dynamics (CFD) results across various conditions. Moreover, PT-PINNs offer a computational efficiency advantage over traditional CFD methods. The total time required to construct the parametric BFS turbulence model is 39 hours, one-sixteenth of the time required by traditional numerical methods. The inference time for a single-condition prediction is just 40 seconds-only 0.5% of a single CFD computation. These findings highlight the potential of PT-PINNs for future applications in engineering turbulence optimization problems.
2503.17709
Yuchen Sun
Yuchen Sun, Shanhui Zhao, Tao Yu, Hao Wen, Samith Va, Mengwei Xu, Yuanchun Li, Chongyang Zhang
GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration
CVPR 2025
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge across diverse software environments. Second, many of them focus solely on navigation tasks, which restricts their capacity to represent comprehensive software architectures and complex user interactions. To address these challenges, we introduce GUI-Xplore, a dataset meticulously designed to enhance cross-application and cross-task generalization via an exploration-and-reasoning framework. GUI-Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities. To fully exploit GUI-Xplore's unique features, we propose Xplore-Agent, a GUI agent framework that combines Action-aware GUI Modeling with Graph-Guided Environment Reasoning. Further experiments indicate that Xplore-Agent achieves a 10% improvement over existing methods in unfamiliar environments, yet there remains significant potential for further enhancement towards truly generalizable GUI agents.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 09:30:37 GMT" } ]
2025-03-25T00:00:00
[ [ "Sun", "Yuchen", "" ], [ "Zhao", "Shanhui", "" ], [ "Yu", "Tao", "" ], [ "Wen", "Hao", "" ], [ "Va", "Samith", "" ], [ "Xu", "Mengwei", "" ], [ "Li", "Yuanchun", "" ], [ "Zhang", "Chongyang", "" ] ]
TITLE: GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration ABSTRACT: GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge across diverse software environments. Second, many of them focus solely on navigation tasks, which restricts their capacity to represent comprehensive software architectures and complex user interactions. To address these challenges, we introduce GUI-Xplore, a dataset meticulously designed to enhance cross-application and cross-task generalization via an exploration-and-reasoning framework. GUI-Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities. To fully exploit GUI-Xplore's unique features, we propose Xplore-Agent, a GUI agent framework that combines Action-aware GUI Modeling with Graph-Guided Environment Reasoning. Further experiments indicate that Xplore-Agent achieves a 10% improvement over existing methods in unfamiliar environments, yet there remains significant potential for further enhancement towards truly generalizable GUI agents.
2503.17712
Heng Gao
Heng Gao, Zhuolin He, Shoumeng Qiu, Xiangyang Xue, Jian Pu
Multi-modality Anomaly Segmentation on the Road
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Semantic segmentation allows autonomous driving cars to understand the surroundings of the vehicle comprehensively. However, it is also crucial for the model to detect obstacles that may jeopardize the safety of autonomous driving systems. Based on our experiments, we find that current uni-modal anomaly segmentation frameworks tend to produce high anomaly scores for non-anomalous regions in images. Motivated by this empirical finding, we develop a multi-modal uncertainty-based anomaly segmentation framework, named MMRAS+, for autonomous driving systems. MMRAS+ effectively reduces the high anomaly outputs of non-anomalous classes by introducing text-modal using the CLIP text encoder. Indeed, MMRAS+ is the first multi-modal anomaly segmentation solution for autonomous driving. Moreover, we develop an ensemble module to further boost the anomaly segmentation performance. Experiments on RoadAnomaly, SMIYC, and Fishyscapes validation datasets demonstrate the superior performance of our method. The code is available in https://github.com/HengGao12/MMRAS_plus.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 09:55:42 GMT" } ]
2025-03-25T00:00:00
[ [ "Gao", "Heng", "" ], [ "He", "Zhuolin", "" ], [ "Qiu", "Shoumeng", "" ], [ "Xue", "Xiangyang", "" ], [ "Pu", "Jian", "" ] ]
TITLE: Multi-modality Anomaly Segmentation on the Road ABSTRACT: Semantic segmentation allows autonomous driving cars to understand the surroundings of the vehicle comprehensively. However, it is also crucial for the model to detect obstacles that may jeopardize the safety of autonomous driving systems. Based on our experiments, we find that current uni-modal anomaly segmentation frameworks tend to produce high anomaly scores for non-anomalous regions in images. Motivated by this empirical finding, we develop a multi-modal uncertainty-based anomaly segmentation framework, named MMRAS+, for autonomous driving systems. MMRAS+ effectively reduces the high anomaly outputs of non-anomalous classes by introducing text-modal using the CLIP text encoder. Indeed, MMRAS+ is the first multi-modal anomaly segmentation solution for autonomous driving. Moreover, we develop an ensemble module to further boost the anomaly segmentation performance. Experiments on RoadAnomaly, SMIYC, and Fishyscapes validation datasets demonstrate the superior performance of our method. The code is available in https://github.com/HengGao12/MMRAS_plus.
2503.17715
Abtin Pourhadi
Abtin Pourhadi and Paul Swoboda
Normalized Matching Transformer
null
null
null
null
cs.CV cs.LG
http://creativecommons.org/licenses/by-nc-sa/4.0/
We present a new state of the art approach for sparse keypoint matching between pairs of images. Our method consists of a fully deep learning based approach combining a visual backbone coupled with a SplineCNN graph neural network for feature processing and a normalized transformer decoder for decoding keypoint correspondences together with the Sinkhorn algorithm. Our method is trained using a contrastive and a hyperspherical loss for better feature representations. We additionally use data augmentation during training. This comparatively simple architecture combining extensive normalization and advanced losses outperforms current state of the art approaches on PascalVOC and SPair-71k datasets by $5.1\%$ and $2.2\%$ respectively compared to BBGM, ASAR, COMMON and GMTR while training for at least $1.7x$ fewer epochs.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 10:09:11 GMT" } ]
2025-03-25T00:00:00
[ [ "Pourhadi", "Abtin", "" ], [ "Swoboda", "Paul", "" ] ]
TITLE: Normalized Matching Transformer ABSTRACT: We present a new state of the art approach for sparse keypoint matching between pairs of images. Our method consists of a fully deep learning based approach combining a visual backbone coupled with a SplineCNN graph neural network for feature processing and a normalized transformer decoder for decoding keypoint correspondences together with the Sinkhorn algorithm. Our method is trained using a contrastive and a hyperspherical loss for better feature representations. We additionally use data augmentation during training. This comparatively simple architecture combining extensive normalization and advanced losses outperforms current state of the art approaches on PascalVOC and SPair-71k datasets by $5.1\%$ and $2.2\%$ respectively compared to BBGM, ASAR, COMMON and GMTR while training for at least $1.7x$ fewer epochs.
2503.17716
Tim Alpherts
Tim Alpherts, Sennay Ghebreab, Nanne van Noord
EMPLACE: Self-Supervised Urban Scene Change Detection
7 pages, 7 figures, published at AAAI 2025
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Urban change is a constant process that influences the perception of neighbourhoods and the lives of the people within them. The field of Urban Scene Change Detection (USCD) aims to capture changes in street scenes using computer vision and can help raise awareness of changes that make it possible to better understand the city and its residents. Traditionally, the field of USCD has used supervised methods with small scale datasets. This constrains methods when applied to new cities, as it requires labour-intensive labeling processes and forces a priori definitions of relevant change. In this paper we introduce AC-1M the largest USCD dataset by far of over 1.1M images, together with EMPLACE, a self-supervising method to train a Vision Transformer using our adaptive triplet loss. We show EMPLACE outperforms SOTA methods both as a pre-training method for linear fine-tuning as well as a zero-shot setting. Lastly, in a case study of Amsterdam, we show that we are able to detect both small and large changes throughout the city and that changes uncovered by EMPLACE, depending on size, correlate with housing prices - which in turn is indicative of inequity.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 10:20:43 GMT" } ]
2025-03-25T00:00:00
[ [ "Alpherts", "Tim", "" ], [ "Ghebreab", "Sennay", "" ], [ "van Noord", "Nanne", "" ] ]
TITLE: EMPLACE: Self-Supervised Urban Scene Change Detection ABSTRACT: Urban change is a constant process that influences the perception of neighbourhoods and the lives of the people within them. The field of Urban Scene Change Detection (USCD) aims to capture changes in street scenes using computer vision and can help raise awareness of changes that make it possible to better understand the city and its residents. Traditionally, the field of USCD has used supervised methods with small scale datasets. This constrains methods when applied to new cities, as it requires labour-intensive labeling processes and forces a priori definitions of relevant change. In this paper we introduce AC-1M the largest USCD dataset by far of over 1.1M images, together with EMPLACE, a self-supervising method to train a Vision Transformer using our adaptive triplet loss. We show EMPLACE outperforms SOTA methods both as a pre-training method for linear fine-tuning as well as a zero-shot setting. Lastly, in a case study of Amsterdam, we show that we are able to detect both small and large changes throughout the city and that changes uncovered by EMPLACE, depending on size, correlate with housing prices - which in turn is indicative of inequity.
2503.17717
Junxian Mu
Yu Wang, Junxian Mu, Hongzhi Huang, Qilong Wang, Pengfei Zhu, Qinghua Hu
BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors
20 pages, 11 figures. Accepted by TPAMI
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers. In this paper, we discuss the aforementioned problem from a new perspective: Can we regularize OSR models without elaborately selecting auxiliary known outliers? We first empirically and theoretically explore the role of foregrounds and backgrounds in open set recognition and disclose that: 1) backgrounds that correlate with foregrounds would mislead the model and cause failures when encounters 'partially' known images; 2) Backgrounds unrelated to foregrounds can serve as auxiliary known outliers and provide regularization via global average pooling. Based on the above insights, we propose a new method, Background Mix (BackMix), that mixes the foreground of an image with different backgrounds to remove the underlying fore-background priors. Specifically, BackMix first estimates the foreground with class activation maps (CAMs), then randomly replaces image patches with backgrounds from other images to obtain mixed images for training. With backgrounds de-correlated from foregrounds, the open set recognition performance is significantly improved. The proposed method is quite simple to implement, requires no extra operation for inferences, and can be seamlessly integrated into almost all of the existing frameworks. The code is released on https://github.com/Vanixxz/BackMix.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 10:23:11 GMT" } ]
2025-03-25T00:00:00
[ [ "Wang", "Yu", "" ], [ "Mu", "Junxian", "" ], [ "Huang", "Hongzhi", "" ], [ "Wang", "Qilong", "" ], [ "Zhu", "Pengfei", "" ], [ "Hu", "Qinghua", "" ] ]
TITLE: BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors ABSTRACT: Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers. In this paper, we discuss the aforementioned problem from a new perspective: Can we regularize OSR models without elaborately selecting auxiliary known outliers? We first empirically and theoretically explore the role of foregrounds and backgrounds in open set recognition and disclose that: 1) backgrounds that correlate with foregrounds would mislead the model and cause failures when encounters 'partially' known images; 2) Backgrounds unrelated to foregrounds can serve as auxiliary known outliers and provide regularization via global average pooling. Based on the above insights, we propose a new method, Background Mix (BackMix), that mixes the foreground of an image with different backgrounds to remove the underlying fore-background priors. Specifically, BackMix first estimates the foreground with class activation maps (CAMs), then randomly replaces image patches with backgrounds from other images to obtain mixed images for training. With backgrounds de-correlated from foregrounds, the open set recognition performance is significantly improved. The proposed method is quite simple to implement, requires no extra operation for inferences, and can be seamlessly integrated into almost all of the existing frameworks. The code is released on https://github.com/Vanixxz/BackMix.
2503.17731
Sungphill Moon
Sungphill Moon, Hyeontae Son, Dongcheol Hur, Sangwook Kim
Co-op: Correspondence-based Novel Object Pose Estimation
Accepted at CVPR 2025
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning. While existing model-based methods suffer from inefficiency due to using a large number of templates, our method enables fast and accurate estimation with a small number of templates. This improvement is achieved by finding semi-dense correspondences between the input image and the pre-rendered templates. Our method achieves strong generalization performance by leveraging a hybrid representation that combines patch-level classification and offset regression. Additionally, our pose refinement model estimates probabilistic flow between the input image and the rendered image, refining the initial estimate to an accurate pose using a differentiable PnP layer. We demonstrate that our method not only estimates object poses rapidly but also outperforms existing methods by a large margin on the seven core datasets of the BOP Challenge, achieving state-of-the-art accuracy.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 11:24:19 GMT" } ]
2025-03-25T00:00:00
[ [ "Moon", "Sungphill", "" ], [ "Son", "Hyeontae", "" ], [ "Hur", "Dongcheol", "" ], [ "Kim", "Sangwook", "" ] ]
TITLE: Co-op: Correspondence-based Novel Object Pose Estimation ABSTRACT: We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning. While existing model-based methods suffer from inefficiency due to using a large number of templates, our method enables fast and accurate estimation with a small number of templates. This improvement is achieved by finding semi-dense correspondences between the input image and the pre-rendered templates. Our method achieves strong generalization performance by leveraging a hybrid representation that combines patch-level classification and offset regression. Additionally, our pose refinement model estimates probabilistic flow between the input image and the rendered image, refining the initial estimate to an accurate pose using a differentiable PnP layer. We demonstrate that our method not only estimates object poses rapidly but also outperforms existing methods by a large margin on the seven core datasets of the BOP Challenge, achieving state-of-the-art accuracy.
2503.17739
Bashar Alhafni
Chatrine Qwaider, Bashar Alhafni, Kirill Chirkunov, Nizar Habash, Ted Briscoe
Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection
null
null
null
null
cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automated Essay Scoring (AES) plays a crucial role in assessing language learners' writing quality, reducing grading workload, and providing real-time feedback. Arabic AES systems are particularly challenged by the lack of annotated essay datasets. This paper presents a novel framework leveraging Large Language Models (LLMs) and Transformers to generate synthetic Arabic essay datasets for AES. We prompt an LLM to generate essays across CEFR proficiency levels and introduce controlled error injection using a fine-tuned Standard Arabic BERT model for error type prediction. Our approach produces realistic human-like essays, contributing a dataset of 3,040 annotated essays. Additionally, we develop a BERT-based auto-marking system for accurate and scalable Arabic essay evaluation. Experimental results demonstrate the effectiveness of our framework in improving Arabic AES performance.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 11:54:10 GMT" } ]
2025-03-25T00:00:00
[ [ "Qwaider", "Chatrine", "" ], [ "Alhafni", "Bashar", "" ], [ "Chirkunov", "Kirill", "" ], [ "Habash", "Nizar", "" ], [ "Briscoe", "Ted", "" ] ]
TITLE: Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection ABSTRACT: Automated Essay Scoring (AES) plays a crucial role in assessing language learners' writing quality, reducing grading workload, and providing real-time feedback. Arabic AES systems are particularly challenged by the lack of annotated essay datasets. This paper presents a novel framework leveraging Large Language Models (LLMs) and Transformers to generate synthetic Arabic essay datasets for AES. We prompt an LLM to generate essays across CEFR proficiency levels and introduce controlled error injection using a fine-tuned Standard Arabic BERT model for error type prediction. Our approach produces realistic human-like essays, contributing a dataset of 3,040 annotated essays. Additionally, we develop a BERT-based auto-marking system for accurate and scalable Arabic essay evaluation. Experimental results demonstrate the effectiveness of our framework in improving Arabic AES performance.
2503.17752
R.D. Lin
R.D. Lin, Pengcheng Weng, Yinqiao Wang, Han Ding, Jinsong Han, Fei Wang
HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving
accepted by CVPR 2025
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Code is available on https://github.com/rdlin118/HiLoTs
[ { "version": "v1", "created": "Sat, 22 Mar 2025 12:29:15 GMT" } ]
2025-03-25T00:00:00
[ [ "Lin", "R. D.", "" ], [ "Weng", "Pengcheng", "" ], [ "Wang", "Yinqiao", "" ], [ "Ding", "Han", "" ], [ "Han", "Jinsong", "" ], [ "Wang", "Fei", "" ] ]
TITLE: HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving ABSTRACT: LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Code is available on https://github.com/rdlin118/HiLoTs
2503.17755
Sharan Maiya
Sharan Maiya, Yinhong Liu, Ramit Debnath, Anna Korhonen
Improving Preference Extraction In LLMs By Identifying Latent Knowledge Through Classifying Probes
preprint, submitted to ACL ARR 2025, 21 pages, 23 figures
null
null
null
cs.CL cs.LG
http://creativecommons.org/licenses/by/4.0/
Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs' latent knowledge and extract more accurate preferences. Through extensive experiments using models of varying size from four different families and six diverse datasets assessing text quality evaluation and common sense reasoning, we demonstrate that both supervised and unsupervised probing approaches consistently outperform traditional generation-based judgement while maintaining similar computational costs. These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. Our results suggest linear probing offers an accurate, robust and computationally efficient approach for LLM-as-judge tasks while providing interpretable insights into how models encode judgement-relevant knowledge. Our data and code will be openly released in the future.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 12:35:25 GMT" } ]
2025-03-25T00:00:00
[ [ "Maiya", "Sharan", "" ], [ "Liu", "Yinhong", "" ], [ "Debnath", "Ramit", "" ], [ "Korhonen", "Anna", "" ] ]
TITLE: Improving Preference Extraction In LLMs By Identifying Latent Knowledge Through Classifying Probes ABSTRACT: Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs' latent knowledge and extract more accurate preferences. Through extensive experiments using models of varying size from four different families and six diverse datasets assessing text quality evaluation and common sense reasoning, we demonstrate that both supervised and unsupervised probing approaches consistently outperform traditional generation-based judgement while maintaining similar computational costs. These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. Our results suggest linear probing offers an accurate, robust and computationally efficient approach for LLM-as-judge tasks while providing interpretable insights into how models encode judgement-relevant knowledge. Our data and code will be openly released in the future.
2503.17770
Jianhua Pei
Yixiang Huang, Jianhua Pei, Luocheng Chen, Zhenchang Du, Jinfu Chen, Zirui Peng
Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model
null
null
null
null
eess.SY cs.SY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, particularly diffusion models, demonstrate exceptional potential in uncertainty quantification for scenario forecasting. Nevertheless, their probabilistic predictive capabilities and conditional bootstrapping mechanisms still remain underexplored. In this paper, a day-ahead probabilistic net load forecasting framework is developed by systematically quantifying epistemic uncertainty and aleatoric variability using the feature-informed enhanced conditional diffusion model (ECDM). The ECDM architecture implements the net load distribution generation process using an imputation-based conditional diffusion model, where multi-modal conditional inputs, such as weather and calendar data, are fused via cross-attention mechanisms. Specifically, historical net load profiles are utilized to guide the reverse diffusion trajectory through non-parametric imputation operators preserving spatial-temporal integrity. To capture periodic characteristics, a novel weekly arrangement method is also introduced, while an unconditional model is integrated to ensure diversity in the generated scenarios. Subsequently, the maximum probabilistic points and probability intervals of predicted net load are obtained by the adaptive kernel density estimation under RES intermittency. Moreover, ECDM is extented to multi-energy forecast framework, attempting to increase interpretability of the net load predictions. Numerical experiments on a publicly available dataset demonstrate the superior forecasting performance of the proposed method compared to existing state-of-the-art approaches.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 13:40:08 GMT" } ]
2025-03-25T00:00:00
[ [ "Huang", "Yixiang", "" ], [ "Pei", "Jianhua", "" ], [ "Chen", "Luocheng", "" ], [ "Du", "Zhenchang", "" ], [ "Chen", "Jinfu", "" ], [ "Peng", "Zirui", "" ] ]
TITLE: Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model ABSTRACT: The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, particularly diffusion models, demonstrate exceptional potential in uncertainty quantification for scenario forecasting. Nevertheless, their probabilistic predictive capabilities and conditional bootstrapping mechanisms still remain underexplored. In this paper, a day-ahead probabilistic net load forecasting framework is developed by systematically quantifying epistemic uncertainty and aleatoric variability using the feature-informed enhanced conditional diffusion model (ECDM). The ECDM architecture implements the net load distribution generation process using an imputation-based conditional diffusion model, where multi-modal conditional inputs, such as weather and calendar data, are fused via cross-attention mechanisms. Specifically, historical net load profiles are utilized to guide the reverse diffusion trajectory through non-parametric imputation operators preserving spatial-temporal integrity. To capture periodic characteristics, a novel weekly arrangement method is also introduced, while an unconditional model is integrated to ensure diversity in the generated scenarios. Subsequently, the maximum probabilistic points and probability intervals of predicted net load are obtained by the adaptive kernel density estimation under RES intermittency. Moreover, ECDM is extented to multi-energy forecast framework, attempting to increase interpretability of the net load predictions. Numerical experiments on a publicly available dataset demonstrate the superior forecasting performance of the proposed method compared to existing state-of-the-art approaches.
2503.17777
Lei Guo
Lei Guo, Wei Chen, Yuxuan Sun, Bo Ai, Nikolaos Pappas, Tony Quek
Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion
Accepted by the WCL
null
null
null
eess.IV cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 14:02:52 GMT" } ]
2025-03-25T00:00:00
[ [ "Guo", "Lei", "" ], [ "Chen", "Wei", "" ], [ "Sun", "Yuxuan", "" ], [ "Ai", "Bo", "" ], [ "Pappas", "Nikolaos", "" ], [ "Quek", "Tony", "" ] ]
TITLE: Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion ABSTRACT: Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks.
2503.17783
Nguyen Phuc Tran
Nguyen Phuc Tran, Brigitte Jaumard, Oscar Delgado
Energy-Aware LLMs: A step towards sustainable AI for downstream applications
This work has been submitted to V. International Conference on Electrical, Computer and Energy Technologies (ICECET 2025) for possible publication
null
null
null
cs.PF cs.AI cs.CL cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high energy consumption. Thus, this research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance for an LLM during fault ticket analysis in communication networks. It further evaluates the pipeline performance using two real-world datasets for the tasks of root cause analysis and response feedback in a communication network. Our results show that an appropriate combination of quantization and pruning techniques is able to reduce energy consumption while significantly improving model performance.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 14:28:29 GMT" } ]
2025-03-25T00:00:00
[ [ "Tran", "Nguyen Phuc", "" ], [ "Jaumard", "Brigitte", "" ], [ "Delgado", "Oscar", "" ] ]
TITLE: Energy-Aware LLMs: A step towards sustainable AI for downstream applications ABSTRACT: Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high energy consumption. Thus, this research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance for an LLM during fault ticket analysis in communication networks. It further evaluates the pipeline performance using two real-world datasets for the tasks of root cause analysis and response feedback in a communication network. Our results show that an appropriate combination of quantization and pruning techniques is able to reduce energy consumption while significantly improving model performance.
2503.17786
Tommaso Di Noto
Tommaso Di Noto, Sofyan Jankowski, Francesco Puccinelli, Guillaume Marie, Sebastien Tourbier, Yasser Aleman-Gomez, Oscar Esteban, Ricardo Corredor-Jerez, Guillaume Saliou, Patric Hagmann, Meritxell Bach Cuadra, Jonas Richiardi
Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study
Paper under review with a Journal in the medical imaging field
null
null
null
eess.IV cs.CV
http://creativecommons.org/licenses/by-sa/4.0/
Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We aim to answer the following questions: 1) Do the readers improve their performance when assisted by the AI algorithm? 2) How much does the AI algorithm impact routine clinical workflow? We reuse and enlarge our open-access, Time-Of-Flight Magnetic Resonance Angiography dataset (N=460). We use 360 subjects for training/validating our algorithm and 100 as unseen test set for the reading session. Even though our model reaches state-of-the-art results on the test set (sensitivity=74%, false positive rate=1.6), we show that neither the junior nor the senior reader significantly increase their sensitivity (p=0.59, p=1, respectively). In addition, we find that reading time for both readers is significantly higher in the "AI-assisted" setting than in the "Unassisted" (+15 seconds, on average; p=3x10^(-4) junior, p=3x10^(-5) senior). The confidence reported by the readers is unchanged across the two settings, indicating that the AI assistance does not influence the certainty of the diagnosis. Our findings highlight the importance of clinical validation of AI algorithms in a clinical setting involving radiologists. This study should serve as a reminder to the community to always examine the real-word effectiveness and workflow impact of proposed algorithms.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 14:32:35 GMT" } ]
2025-03-25T00:00:00
[ [ "Di Noto", "Tommaso", "" ], [ "Jankowski", "Sofyan", "" ], [ "Puccinelli", "Francesco", "" ], [ "Marie", "Guillaume", "" ], [ "Tourbier", "Sebastien", "" ], [ "Aleman-Gomez", "Yasser", "" ], [ "Esteban", "Oscar", "" ], [ "Corredor-Jerez", "Ricardo", "" ], [ "Saliou", "Guillaume", "" ], [ "Hagmann", "Patric", "" ], [ "Cuadra", "Meritxell Bach", "" ], [ "Richiardi", "Jonas", "" ] ]
TITLE: Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study ABSTRACT: Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We aim to answer the following questions: 1) Do the readers improve their performance when assisted by the AI algorithm? 2) How much does the AI algorithm impact routine clinical workflow? We reuse and enlarge our open-access, Time-Of-Flight Magnetic Resonance Angiography dataset (N=460). We use 360 subjects for training/validating our algorithm and 100 as unseen test set for the reading session. Even though our model reaches state-of-the-art results on the test set (sensitivity=74%, false positive rate=1.6), we show that neither the junior nor the senior reader significantly increase their sensitivity (p=0.59, p=1, respectively). In addition, we find that reading time for both readers is significantly higher in the "AI-assisted" setting than in the "Unassisted" (+15 seconds, on average; p=3x10^(-4) junior, p=3x10^(-5) senior). The confidence reported by the readers is unchanged across the two settings, indicating that the AI assistance does not influence the certainty of the diagnosis. Our findings highlight the importance of clinical validation of AI algorithms in a clinical setting involving radiologists. This study should serve as a reminder to the community to always examine the real-word effectiveness and workflow impact of proposed algorithms.
2503.17788
Gaoge Han
Gaoge Han, Yongkang Cheng, Zhe Chen, Shaoli Huang, Tongliang Liu
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures and occlusions, causing significant difficulty in achieving plausible interaction alignment. Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts. To tackle this, we propose a novel framework that attempts to precisely align hand poses and interactions by synergistically integrating foundation model-driven 2D priors with diffusion-based interaction refinement for occlusion-resistant two-hand reconstruction. First, we introduce a Fusion Alignment Encoder that learns to align fused multimodal priors keypoints, segmentation maps, and depth cues from foundation models during training. This provides robust structured guidance, further enabling efficient inference without foundation models at test time while maintaining high reconstruction accuracy. Second, we employ a two-hand diffusion model explicitly trained to transform interpenetrated poses into plausible, non-penetrated interactions, leveraging gradient-guided denoising to correct artifacts and ensure realistic spatial relations. Extensive evaluations demonstrate that our method achieves state-of-the-art performance on InterHand2.6M, FreiHAND, and HIC datasets, significantly advancing occlusion handling and interaction robustness.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 14:42:27 GMT" } ]
2025-03-25T00:00:00
[ [ "Han", "Gaoge", "" ], [ "Cheng", "Yongkang", "" ], [ "Chen", "Zhe", "" ], [ "Huang", "Shaoli", "" ], [ "Liu", "Tongliang", "" ] ]
TITLE: Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction ABSTRACT: Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures and occlusions, causing significant difficulty in achieving plausible interaction alignment. Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts. To tackle this, we propose a novel framework that attempts to precisely align hand poses and interactions by synergistically integrating foundation model-driven 2D priors with diffusion-based interaction refinement for occlusion-resistant two-hand reconstruction. First, we introduce a Fusion Alignment Encoder that learns to align fused multimodal priors keypoints, segmentation maps, and depth cues from foundation models during training. This provides robust structured guidance, further enabling efficient inference without foundation models at test time while maintaining high reconstruction accuracy. Second, we employ a two-hand diffusion model explicitly trained to transform interpenetrated poses into plausible, non-penetrated interactions, leveraging gradient-guided denoising to correct artifacts and ensure realistic spatial relations. Extensive evaluations demonstrate that our method achieves state-of-the-art performance on InterHand2.6M, FreiHAND, and HIC datasets, significantly advancing occlusion handling and interaction robustness.
2503.17799
Ramakanth Kavuluru
Yuhang Jiang and Ramakanth Kavuluru
Relation Extraction with Instance-Adapted Predicate Descriptions
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
Relation extraction (RE) is a standard information extraction task playing a major role in downstream applications such as knowledge discovery and question answering. Although decoder-only large language models are excelling in generative tasks, smaller encoder models are still the go to architecture for RE. In this paper, we revisit fine-tuning such smaller models using a novel dual-encoder architecture with a joint contrastive and cross-entropy loss. Unlike previous methods that employ a fixed linear layer for predicate representations, our approach uses a second encoder to compute instance-specific predicate representations by infusing them with real entity spans from corresponding input instances. We conducted experiments on two biomedical RE datasets and two general domain datasets. Our approach achieved F1 score improvements ranging from 1% to 2% over state-of-the-art methods with a simple but elegant formulation. Ablation studies justify the importance of various components built into the proposed architecture.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 15:36:41 GMT" } ]
2025-03-25T00:00:00
[ [ "Jiang", "Yuhang", "" ], [ "Kavuluru", "Ramakanth", "" ] ]
TITLE: Relation Extraction with Instance-Adapted Predicate Descriptions ABSTRACT: Relation extraction (RE) is a standard information extraction task playing a major role in downstream applications such as knowledge discovery and question answering. Although decoder-only large language models are excelling in generative tasks, smaller encoder models are still the go to architecture for RE. In this paper, we revisit fine-tuning such smaller models using a novel dual-encoder architecture with a joint contrastive and cross-entropy loss. Unlike previous methods that employ a fixed linear layer for predicate representations, our approach uses a second encoder to compute instance-specific predicate representations by infusing them with real entity spans from corresponding input instances. We conducted experiments on two biomedical RE datasets and two general domain datasets. Our approach achieved F1 score improvements ranging from 1% to 2% over state-of-the-art methods with a simple but elegant formulation. Ablation studies justify the importance of various components built into the proposed architecture.
2503.17809
Zheng Tracy Ke
Morgane Austern, Yuanchuan Guo, Zheng Tracy Ke, Tianle Liu
Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models
35 pages, 9 figures, 3 tables
null
null
null
stat.ML cs.LG math.ST stat.TH
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextualized word embeddings, which capture deeper meaning and relationships between words. We aim to leverage such embeddings to improve topic modeling. We use a pre-trained LLM to convert each document into a sequence of word embeddings. This sequence is then modeled as a Poisson point process, with its intensity measure expressed as a convex combination of $K$ base measures, each corresponding to a topic. To estimate these topics, we propose a flexible algorithm that integrates traditional topic modeling methods, enhanced by net-rounding applied before and kernel smoothing applied after. One advantage of this framework is that it treats the LLM as a black box, requiring no fine-tuning of its parameters. Another advantage is its ability to seamlessly integrate any traditional topic modeling approach as a plug-in module, without the need for modifications Assuming each topic is a $\beta$-H\"{o}lder smooth intensity measure on the embedded space, we establish the rate of convergence of our method. We also provide a minimax lower bound and show that the rate of our method matches with the lower bound when $\beta\leq 1$. Additionally, we apply our method to several datasets, providing evidence that it offers an advantage over traditional topic modeling approaches.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 16:19:04 GMT" } ]
2025-03-25T00:00:00
[ [ "Austern", "Morgane", "" ], [ "Guo", "Yuanchuan", "" ], [ "Ke", "Zheng Tracy", "" ], [ "Liu", "Tianle", "" ] ]
TITLE: Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models ABSTRACT: Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextualized word embeddings, which capture deeper meaning and relationships between words. We aim to leverage such embeddings to improve topic modeling. We use a pre-trained LLM to convert each document into a sequence of word embeddings. This sequence is then modeled as a Poisson point process, with its intensity measure expressed as a convex combination of $K$ base measures, each corresponding to a topic. To estimate these topics, we propose a flexible algorithm that integrates traditional topic modeling methods, enhanced by net-rounding applied before and kernel smoothing applied after. One advantage of this framework is that it treats the LLM as a black box, requiring no fine-tuning of its parameters. Another advantage is its ability to seamlessly integrate any traditional topic modeling approach as a plug-in module, without the need for modifications Assuming each topic is a $\beta$-H\"{o}lder smooth intensity measure on the embedded space, we establish the rate of convergence of our method. We also provide a minimax lower bound and show that the rate of our method matches with the lower bound when $\beta\leq 1$. Additionally, we apply our method to several datasets, providing evidence that it offers an advantage over traditional topic modeling approaches.
2503.17814
Wen Li
Wen Li, Chen Liu, Shangshu Yu, Dunqiang Liu, Yin Zhou, Siqi Shen, Chenglu Wen and Cheng Wang
LightLoc: Learning Outdoor LiDAR Localization at Light Speed
CVPR 2025
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these methods impractical for time-sensitive applications, such as autonomous driving, drones, and robotics. We identify large coverage areas and vast data in large-scale outdoor scenes as key challenges that limit fast training. In this paper, we propose LightLoc, the first method capable of efficiently learning localization in a new scene at light speed. LightLoc introduces two novel techniques to address these challenges. First, we introduce sample classification guidance to assist regression learning, reducing ambiguity from similar samples and improving training efficiency. Second, we propose redundant sample downsampling to remove well-learned frames during training, reducing training time without compromising accuracy. Additionally, the fast training and confidence estimation capabilities of sample classification enable its integration into SLAM, effectively eliminating error accumulation. Extensive experiments on large-scale outdoor datasets demonstrate that LightLoc achieves state-of-the-art performance with a 50x reduction in training time than existing methods. Our code is available at https://github.com/liw95/LightLoc.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 16:33:41 GMT" } ]
2025-03-25T00:00:00
[ [ "Li", "Wen", "" ], [ "Liu", "Chen", "" ], [ "Yu", "Shangshu", "" ], [ "Liu", "Dunqiang", "" ], [ "Zhou", "Yin", "" ], [ "Shen", "Siqi", "" ], [ "Wen", "Chenglu", "" ], [ "Wang", "Cheng", "" ] ]
TITLE: LightLoc: Learning Outdoor LiDAR Localization at Light Speed ABSTRACT: Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these methods impractical for time-sensitive applications, such as autonomous driving, drones, and robotics. We identify large coverage areas and vast data in large-scale outdoor scenes as key challenges that limit fast training. In this paper, we propose LightLoc, the first method capable of efficiently learning localization in a new scene at light speed. LightLoc introduces two novel techniques to address these challenges. First, we introduce sample classification guidance to assist regression learning, reducing ambiguity from similar samples and improving training efficiency. Second, we propose redundant sample downsampling to remove well-learned frames during training, reducing training time without compromising accuracy. Additionally, the fast training and confidence estimation capabilities of sample classification enable its integration into SLAM, effectively eliminating error accumulation. Extensive experiments on large-scale outdoor datasets demonstrate that LightLoc achieves state-of-the-art performance with a 50x reduction in training time than existing methods. Our code is available at https://github.com/liw95/LightLoc.
2503.17820
Zheng Lin
Zheng Lin, Nan Zhou, Chen-Xi Du, Deng-Ping Fan, Shi-Min Hu
RefCut: Interactive Segmentation with Reference Guidance
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Interactive segmentation aims to segment the specified target on the image with positive and negative clicks from users. Interactive ambiguity is a crucial issue in this field, which refers to the possibility of multiple compliant outcomes with the same clicks, such as selecting a part of an object versus the entire object, a single object versus a combination of multiple objects, and so on. The existing methods cannot provide intuitive guidance to the model, which leads to unstable output results and makes it difficult to meet the large-scale and efficient annotation requirements for specific targets in some scenarios. To bridge this gap, we introduce RefCut, a reference-based interactive segmentation framework designed to address part ambiguity and object ambiguity in segmenting specific targets. Users only need to provide a reference image and corresponding reference masks, and the model will be optimized based on them, which greatly reduces the interactive burden on users when annotating a large number of such targets. In addition, to enrich these two kinds of ambiguous data, we propose a new Target Disassembly Dataset which contains two subsets of part disassembly and object disassembly for evaluation. In the combination evaluation of multiple datasets, our RefCut achieved state-of-the-art performance. Extensive experiments and visualized results demonstrate that RefCut advances the field of intuitive and controllable interactive segmentation. Our code will be publicly available and the demo video is in https://www.lin-zheng.com/refcut.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 17:14:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Lin", "Zheng", "" ], [ "Zhou", "Nan", "" ], [ "Du", "Chen-Xi", "" ], [ "Fan", "Deng-Ping", "" ], [ "Hu", "Shi-Min", "" ] ]
TITLE: RefCut: Interactive Segmentation with Reference Guidance ABSTRACT: Interactive segmentation aims to segment the specified target on the image with positive and negative clicks from users. Interactive ambiguity is a crucial issue in this field, which refers to the possibility of multiple compliant outcomes with the same clicks, such as selecting a part of an object versus the entire object, a single object versus a combination of multiple objects, and so on. The existing methods cannot provide intuitive guidance to the model, which leads to unstable output results and makes it difficult to meet the large-scale and efficient annotation requirements for specific targets in some scenarios. To bridge this gap, we introduce RefCut, a reference-based interactive segmentation framework designed to address part ambiguity and object ambiguity in segmenting specific targets. Users only need to provide a reference image and corresponding reference masks, and the model will be optimized based on them, which greatly reduces the interactive burden on users when annotating a large number of such targets. In addition, to enrich these two kinds of ambiguous data, we propose a new Target Disassembly Dataset which contains two subsets of part disassembly and object disassembly for evaluation. In the combination evaluation of multiple datasets, our RefCut achieved state-of-the-art performance. Extensive experiments and visualized results demonstrate that RefCut advances the field of intuitive and controllable interactive segmentation. Our code will be publicly available and the demo video is in https://www.lin-zheng.com/refcut.
2503.17831
Qingshan Hou
Qingshan Hou, Meng Wang, Peng Cao, Zou Ke, Xiaoli Liu, Huazhu Fu, Osmar R. Zaiane
FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation
null
null
null
null
eess.IV cs.AI cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49\% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 18:08:07 GMT" } ]
2025-03-25T00:00:00
[ [ "Hou", "Qingshan", "" ], [ "Wang", "Meng", "" ], [ "Cao", "Peng", "" ], [ "Ke", "Zou", "" ], [ "Liu", "Xiaoli", "" ], [ "Fu", "Huazhu", "" ], [ "Zaiane", "Osmar R.", "" ] ]
TITLE: FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation ABSTRACT: Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training, creating significant barriers for development and deployment. To address this critical challenge, we propose FundusGAN, a novel hierarchical feature-aware generative framework specifically designed for high-fidelity fundus image synthesis. Our approach leverages a Feature Pyramid Network within its encoder to comprehensively extract multi-scale information, capturing both large anatomical structures and subtle pathological features. The framework incorporates a modified StyleGAN-based generator with dilated convolutions and strategic upsampling adjustments to preserve critical retinal structures while enhancing pathological detail representation. Comprehensive evaluations on the DDR, DRIVE, and IDRiD datasets demonstrate that FundusGAN consistently outperforms state-of-the-art methods across multiple metrics (SSIM: 0.8863, FID: 54.2, KID: 0.0436 on DDR). Furthermore, disease classification experiments reveal that augmenting training data with FundusGAN-generated images significantly improves diagnostic accuracy across multiple CNN architectures (up to 6.49\% improvement with ResNet50). These results establish FundusGAN as a valuable foundation model component that effectively addresses data scarcity challenges in ophthalmological AI research, enabling more robust and generalizable diagnostic systems while reducing dependency on large-scale clinical data collection.
2503.17842
Maryam Abdolali
Maryam Abdolali, Romina Zakerian, Behnam Roshanfekr, Fardin Ayar, Mohammad Rahmati
Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks
null
null
null
null
cs.LG cs.AI
http://creativecommons.org/licenses/by/4.0/
In this paper, we propose a novel framework that combines ensemble learning with augmented graph structures to improve the performance and robustness of semi-supervised node classification in graphs. By creating multiple augmented views of the same graph, our approach harnesses the "wisdom of a diverse crowd", mitigating the challenges posed by noisy graph structures. Leveraging ensemble learning allows us to simultaneously achieve three key goals: adaptive confidence threshold selection based on model agreement, dynamic determination of the number of high-confidence samples for training, and robust extraction of pseudo-labels to mitigate confirmation bias. Our approach uniquely integrates adaptive ensemble consensus to flexibly guide pseudo-label extraction and sample selection, reducing the risks of error accumulation and improving robustness. Furthermore, the use of ensemble-driven consensus for pseudo-labeling captures subtle patterns that individual models often overlook, enabling the model to generalize better. Experiments on several real-world datasets demonstrate the effectiveness of our proposed method.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 19:10:54 GMT" } ]
2025-03-25T00:00:00
[ [ "Abdolali", "Maryam", "" ], [ "Zakerian", "Romina", "" ], [ "Roshanfekr", "Behnam", "" ], [ "Ayar", "Fardin", "" ], [ "Rahmati", "Mohammad", "" ] ]
TITLE: Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks ABSTRACT: In this paper, we propose a novel framework that combines ensemble learning with augmented graph structures to improve the performance and robustness of semi-supervised node classification in graphs. By creating multiple augmented views of the same graph, our approach harnesses the "wisdom of a diverse crowd", mitigating the challenges posed by noisy graph structures. Leveraging ensemble learning allows us to simultaneously achieve three key goals: adaptive confidence threshold selection based on model agreement, dynamic determination of the number of high-confidence samples for training, and robust extraction of pseudo-labels to mitigate confirmation bias. Our approach uniquely integrates adaptive ensemble consensus to flexibly guide pseudo-label extraction and sample selection, reducing the risks of error accumulation and improving robustness. Furthermore, the use of ensemble-driven consensus for pseudo-labeling captures subtle patterns that individual models often overlook, enabling the model to generalize better. Experiments on several real-world datasets demonstrate the effectiveness of our proposed method.
2503.17855
Lev Utkin
Andrei V. Konstantinov and Lev V. Utkin
A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
null
null
null
null
cs.LG stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions using the first and second derivatives of the loss function, enabling the optimization of complex tasks such as classification, regression, and survival analysis. We demonstrate the method's applicability to classification, regression, and survival analysis tasks, including those with censored data. Numerical experiments on both real and synthetic datasets compare the proposed method with traditional decision tree algorithms, such as CART, Extremely Randomized Trees, and SurvTree. The implementation of the method is publicly available, providing a practical tool for researchers and practitioners. This work advances the field of decision tree-based modeling, offering a more flexible and accurate approach for handling structured data and complex tasks. By leveraging gradient-based optimization, the proposed method bridges the gap between traditional decision trees and modern machine learning techniques, paving the way for further innovations in interpretable and high-performing models.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 20:25:30 GMT" } ]
2025-03-25T00:00:00
[ [ "Konstantinov", "Andrei V.", "" ], [ "Utkin", "Lev V.", "" ] ]
TITLE: A novel gradient-based method for decision trees optimizing arbitrary differential loss functions ABSTRACT: There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions using the first and second derivatives of the loss function, enabling the optimization of complex tasks such as classification, regression, and survival analysis. We demonstrate the method's applicability to classification, regression, and survival analysis tasks, including those with censored data. Numerical experiments on both real and synthetic datasets compare the proposed method with traditional decision tree algorithms, such as CART, Extremely Randomized Trees, and SurvTree. The implementation of the method is publicly available, providing a practical tool for researchers and practitioners. This work advances the field of decision tree-based modeling, offering a more flexible and accurate approach for handling structured data and complex tasks. By leveraging gradient-based optimization, the proposed method bridges the gap between traditional decision trees and modern machine learning techniques, paving the way for further innovations in interpretable and high-performing models.
2503.17856
Radu Beche
Radu Beche, Sergiu Nedevschi
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling
Currently under review
null
null
null
cs.CV
http://creativecommons.org/licenses/by-sa/4.0/
The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene compositions, and rendering artifacts that compromise real-world applicability. We introduce ClaraVid, a synthetic aerial dataset specifically designed to overcome these limitations. Comprising 16,917 high-resolution images captured at 4032x3024 from multiple viewpoints across diverse landscapes, ClaraVid provides dense depth maps, panoptic segmentation, sparse point clouds, and dynamic object masks, while mitigating common rendering artifacts. To further advance neural reconstruction, we introduce the Delentropic Scene Profile (DSP), a novel complexity metric derived from differential entropy analysis, designed to quantitatively assess scene difficulty and inform reconstruction tasks. Utilizing DSP, we systematically benchmark neural reconstruction methods, uncovering a consistent, measurable correlation between scene complexity and reconstruction accuracy. Empirical results indicate that higher delentropy strongly correlates with increased reconstruction errors, validating DSP as a reliable complexity prior. Currently under review, upon acceptance the data and code will be available at $\href{https://rdbch.github.io/claravid}{rdbch.github.io/ClaraVid}$.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 20:26:20 GMT" } ]
2025-03-25T00:00:00
[ [ "Beche", "Radu", "" ], [ "Nedevschi", "Sergiu", "" ] ]
TITLE: ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling ABSTRACT: The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene compositions, and rendering artifacts that compromise real-world applicability. We introduce ClaraVid, a synthetic aerial dataset specifically designed to overcome these limitations. Comprising 16,917 high-resolution images captured at 4032x3024 from multiple viewpoints across diverse landscapes, ClaraVid provides dense depth maps, panoptic segmentation, sparse point clouds, and dynamic object masks, while mitigating common rendering artifacts. To further advance neural reconstruction, we introduce the Delentropic Scene Profile (DSP), a novel complexity metric derived from differential entropy analysis, designed to quantitatively assess scene difficulty and inform reconstruction tasks. Utilizing DSP, we systematically benchmark neural reconstruction methods, uncovering a consistent, measurable correlation between scene complexity and reconstruction accuracy. Empirical results indicate that higher delentropy strongly correlates with increased reconstruction errors, validating DSP as a reliable complexity prior. Currently under review, upon acceptance the data and code will be available at $\href{https://rdbch.github.io/claravid}{rdbch.github.io/ClaraVid}$.
2503.17860
Felix Faltings
Felix Faltings, Wei Wei, Yujia Bao
Enhancing Retrieval Systems with Inference-Time Logical Reasoning
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
Traditional retrieval methods rely on transforming user queries into vector representations and retrieving documents based on cosine similarity within an embedding space. While efficient and scalable, this approach often fails to handle complex queries involving logical constructs such as negations, conjunctions, and disjunctions. In this paper, we propose a novel inference-time logical reasoning framework that explicitly incorporates logical reasoning into the retrieval process. Our method extracts logical reasoning structures from natural language queries and then composes the individual cosine similarity scores to formulate the final document scores. This approach enables the retrieval process to handle complex logical reasoning without compromising computational efficiency. Our results on both synthetic and real-world benchmarks demonstrate that the proposed method consistently outperforms traditional retrieval methods across different models and datasets, significantly improving retrieval performance for complex queries.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 20:40:18 GMT" } ]
2025-03-25T00:00:00
[ [ "Faltings", "Felix", "" ], [ "Wei", "Wei", "" ], [ "Bao", "Yujia", "" ] ]
TITLE: Enhancing Retrieval Systems with Inference-Time Logical Reasoning ABSTRACT: Traditional retrieval methods rely on transforming user queries into vector representations and retrieving documents based on cosine similarity within an embedding space. While efficient and scalable, this approach often fails to handle complex queries involving logical constructs such as negations, conjunctions, and disjunctions. In this paper, we propose a novel inference-time logical reasoning framework that explicitly incorporates logical reasoning into the retrieval process. Our method extracts logical reasoning structures from natural language queries and then composes the individual cosine similarity scores to formulate the final document scores. This approach enables the retrieval process to handle complex logical reasoning without compromising computational efficiency. Our results on both synthetic and real-world benchmarks demonstrate that the proposed method consistently outperforms traditional retrieval methods across different models and datasets, significantly improving retrieval performance for complex queries.
2503.17867
Paul Irofti
Alexandru Apostu, Silviu Gheorghe, Andrei H\^iji, Nicolae Cleju, Andrei P\u{a}tra\c{s}cu, Cristian Rusu, Radu Ionescu, Paul Irofti
Detecting and Mitigating DDoS Attacks with AI: A Survey
null
null
null
null
cs.CR cs.AI cs.LG cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 21:54:23 GMT" } ]
2025-03-25T00:00:00
[ [ "Apostu", "Alexandru", "" ], [ "Gheorghe", "Silviu", "" ], [ "Hîji", "Andrei", "" ], [ "Cleju", "Nicolae", "" ], [ "Pătraşcu", "Andrei", "" ], [ "Rusu", "Cristian", "" ], [ "Ionescu", "Radu", "" ], [ "Irofti", "Paul", "" ] ]
TITLE: Detecting and Mitigating DDoS Attacks with AI: A Survey ABSTRACT: Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed.
2503.17871
Abby Stylianou
Pranavi Kolouju, Eric Xing, Robert Pless, Nathan Jacobs, Abby Stylianou
good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval
null
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision-language models have improved CIR, but dataset limitations remain a barrier. Existing datasets often rely on simplistic, ambiguous, or insufficient manual annotations, hindering fine-grained retrieval. We introduce good4cir, a structured pipeline leveraging vision-language models to generate high-quality synthetic annotations. Our method involves: (1) extracting fine-grained object descriptions from query images, (2) generating comparable descriptions for target images, and (3) synthesizing textual instructions capturing meaningful transformations between images. This reduces hallucination, enhances modification diversity, and ensures object-level consistency. Applying our method improves existing datasets and enables creating new datasets across diverse domains. Results demonstrate improved retrieval accuracy for CIR models trained on our pipeline-generated datasets. We release our dataset construction framework to support further research in CIR and multi-modal retrieval.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 22:33:56 GMT" } ]
2025-03-25T00:00:00
[ [ "Kolouju", "Pranavi", "" ], [ "Xing", "Eric", "" ], [ "Pless", "Robert", "" ], [ "Jacobs", "Nathan", "" ], [ "Stylianou", "Abby", "" ] ]
TITLE: good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval ABSTRACT: Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision-language models have improved CIR, but dataset limitations remain a barrier. Existing datasets often rely on simplistic, ambiguous, or insufficient manual annotations, hindering fine-grained retrieval. We introduce good4cir, a structured pipeline leveraging vision-language models to generate high-quality synthetic annotations. Our method involves: (1) extracting fine-grained object descriptions from query images, (2) generating comparable descriptions for target images, and (3) synthesizing textual instructions capturing meaningful transformations between images. This reduces hallucination, enhances modification diversity, and ensures object-level consistency. Applying our method improves existing datasets and enables creating new datasets across diverse domains. Results demonstrate improved retrieval accuracy for CIR models trained on our pipeline-generated datasets. We release our dataset construction framework to support further research in CIR and multi-modal retrieval.
2503.17876
Kaiwen Zuo
Kaiwen Zuo, Jing Tang, Hanbing Qin, Binli Luo, Ligang He, Shiyan Tang
Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning
The 46th European Conference on Information Retrieval Workshop
null
null
null
cs.CL cs.IR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model's capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method's effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework's superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 23:01:07 GMT" } ]
2025-03-25T00:00:00
[ [ "Zuo", "Kaiwen", "" ], [ "Tang", "Jing", "" ], [ "Qin", "Hanbing", "" ], [ "Luo", "Binli", "" ], [ "He", "Ligang", "" ], [ "Tang", "Shiyan", "" ] ]
TITLE: Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning ABSTRACT: Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model's capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method's effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework's superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations.
2503.17877
Samira Alkaee Taleghan
Samira Alkaee Taleghan, Andrew P. Barrett, Walter N. Meier, Farnoush Banaei-Kashani
IceBench: A Benchmark for Deep Learning based Sea Ice Type Classification
null
null
null
null
cs.CV cs.LG
http://creativecommons.org/licenses/by/4.0/
Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While both traditional and deep learning approaches have been explored, deep learning models offer a promising direction for improving efficiency and consistency in sea ice classification. However, the absence of a standardized benchmark and comparative study prevents a clear consensus on the best-performing models. To bridge this gap, we introduce \textit{IceBench}, a comprehensive benchmarking framework for sea ice type classification. Our key contributions are threefold: First, we establish the IceBench benchmarking framework which leverages the existing AI4Arctic Sea Ice Challenge dataset as a standardized dataset, incorporates a comprehensive set of evaluation metrics, and includes representative models from the entire spectrum of sea ice type classification methods categorized in two distinct groups, namely, pixel-based classification methods and patch-based classification methods. IceBench is open-source and allows for convenient integration and evaluation of other sea ice type classification methods; hence, facilitating comparative evaluation of new methods and improving reproducibility in the field. Second, we conduct an in-depth comparative study on representative models to assess their strengths and limitations, providing insights for both practitioners and researchers. Third, we leverage IceBench for systematic experiments addressing key research questions on model transferability across seasons (time) and locations (space), data downscaling, and preprocessing strategies.
[ { "version": "v1", "created": "Sat, 22 Mar 2025 23:14:50 GMT" } ]
2025-03-25T00:00:00
[ [ "Taleghan", "Samira Alkaee", "" ], [ "Barrett", "Andrew P.", "" ], [ "Meier", "Walter N.", "" ], [ "Banaei-Kashani", "Farnoush", "" ] ]
TITLE: IceBench: A Benchmark for Deep Learning based Sea Ice Type Classification ABSTRACT: Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While both traditional and deep learning approaches have been explored, deep learning models offer a promising direction for improving efficiency and consistency in sea ice classification. However, the absence of a standardized benchmark and comparative study prevents a clear consensus on the best-performing models. To bridge this gap, we introduce \textit{IceBench}, a comprehensive benchmarking framework for sea ice type classification. Our key contributions are threefold: First, we establish the IceBench benchmarking framework which leverages the existing AI4Arctic Sea Ice Challenge dataset as a standardized dataset, incorporates a comprehensive set of evaluation metrics, and includes representative models from the entire spectrum of sea ice type classification methods categorized in two distinct groups, namely, pixel-based classification methods and patch-based classification methods. IceBench is open-source and allows for convenient integration and evaluation of other sea ice type classification methods; hence, facilitating comparative evaluation of new methods and improving reproducibility in the field. Second, we conduct an in-depth comparative study on representative models to assess their strengths and limitations, providing insights for both practitioners and researchers. Third, we leverage IceBench for systematic experiments addressing key research questions on model transferability across seasons (time) and locations (space), data downscaling, and preprocessing strategies.
2503.17885
Arastoo Zibaeirad
Arastoo Zibaeirad, Marco Vieira
Reasoning with LLMs for Zero-Shot Vulnerability Detection
null
null
null
null
cs.SE cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automating software vulnerability detection (SVD) remains a critical challenge in an era of increasingly complex and interdependent software systems. Despite significant advances in Large Language Models (LLMs) for code analysis, prevailing evaluation methodologies often lack the \textbf{context-aware robustness} necessary to capture real-world intricacies and cross-component interactions. To address these limitations, we present \textbf{VulnSage}, a comprehensive evaluation framework and a dataset curated from diverse, large-scale open-source system software projects developed in C/C++. Unlike prior datasets, it leverages a heuristic noise pre-filtering approach combined with LLM-based reasoning to ensure a representative and minimally noisy spectrum of vulnerabilities. The framework supports multi-granular analysis across function, file, and inter-function levels and employs four diverse zero-shot prompt strategies: Baseline, Chain-of-Thought, Think, and Think & Verify. Through this evaluation, we uncover that structured reasoning prompts substantially improve LLM performance, with Think & Verify reducing ambiguous responses from 20.3% to 9.1% while increasing accuracy. We further demonstrate that code-specialized models consistently outperform general-purpose alternatives, with performance varying significantly across vulnerability types, revealing that no single approach universally excels across all security contexts. Link to dataset and codes: https://github.com/Erroristotle/VulnSage.git
[ { "version": "v1", "created": "Sat, 22 Mar 2025 23:59:17 GMT" } ]
2025-03-25T00:00:00
[ [ "Zibaeirad", "Arastoo", "" ], [ "Vieira", "Marco", "" ] ]
TITLE: Reasoning with LLMs for Zero-Shot Vulnerability Detection ABSTRACT: Automating software vulnerability detection (SVD) remains a critical challenge in an era of increasingly complex and interdependent software systems. Despite significant advances in Large Language Models (LLMs) for code analysis, prevailing evaluation methodologies often lack the \textbf{context-aware robustness} necessary to capture real-world intricacies and cross-component interactions. To address these limitations, we present \textbf{VulnSage}, a comprehensive evaluation framework and a dataset curated from diverse, large-scale open-source system software projects developed in C/C++. Unlike prior datasets, it leverages a heuristic noise pre-filtering approach combined with LLM-based reasoning to ensure a representative and minimally noisy spectrum of vulnerabilities. The framework supports multi-granular analysis across function, file, and inter-function levels and employs four diverse zero-shot prompt strategies: Baseline, Chain-of-Thought, Think, and Think & Verify. Through this evaluation, we uncover that structured reasoning prompts substantially improve LLM performance, with Think & Verify reducing ambiguous responses from 20.3% to 9.1% while increasing accuracy. We further demonstrate that code-specialized models consistently outperform general-purpose alternatives, with performance varying significantly across vulnerability types, revealing that no single approach universally excels across all security contexts. Link to dataset and codes: https://github.com/Erroristotle/VulnSage.git
2503.17899
Dongheng Lin
Dongheng Lin, Han Hu, Jianbo Jiao
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Time becomes visible through illumination changes in what we see. Inspired by this, in this paper we explore the potential to learn time awareness from static images, trying to answer: what time tells us? To this end, we first introduce a Time-Oriented Collection (TOC) dataset, which contains 130,906 images with reliable timestamps. Leveraging this dataset, we propose a Time-Image Contrastive Learning (TICL) approach to jointly model timestamps and related visual representations through cross-modal contrastive learning. We found that the proposed TICL, 1) not only achieves state-of-the-art performance on the timestamp estimation task, over various benchmark metrics, 2) but also, interestingly, though only seeing static images, the time-aware embeddings learned from TICL show strong capability in several time-aware downstream tasks such as time-based image retrieval, video scene classification, and time-aware image editing. Our findings suggest that time-related visual cues can be learned from static images and are beneficial for various vision tasks, laying a foundation for future research on understanding time-related visual context. Project page:https://rathgrith.github.io/timetells/.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 01:56:35 GMT" } ]
2025-03-25T00:00:00
[ [ "Lin", "Dongheng", "" ], [ "Hu", "Han", "" ], [ "Jiao", "Jianbo", "" ] ]
TITLE: What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images ABSTRACT: Time becomes visible through illumination changes in what we see. Inspired by this, in this paper we explore the potential to learn time awareness from static images, trying to answer: what time tells us? To this end, we first introduce a Time-Oriented Collection (TOC) dataset, which contains 130,906 images with reliable timestamps. Leveraging this dataset, we propose a Time-Image Contrastive Learning (TICL) approach to jointly model timestamps and related visual representations through cross-modal contrastive learning. We found that the proposed TICL, 1) not only achieves state-of-the-art performance on the timestamp estimation task, over various benchmark metrics, 2) but also, interestingly, though only seeing static images, the time-aware embeddings learned from TICL show strong capability in several time-aware downstream tasks such as time-based image retrieval, video scene classification, and time-aware image editing. Our findings suggest that time-related visual cues can be learned from static images and are beneficial for various vision tasks, laying a foundation for future research on understanding time-related visual context. Project page:https://rathgrith.github.io/timetells/.
2503.17903
Yali Fu
Yali Fu, Jindong Li, Qi Wang, Qianli Xing
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
null
null
null
null
cs.LG cs.AI
http://creativecommons.org/licenses/by/4.0/
Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture the long-range dependencies efficiently and neglect the spectral information. Recently, selective State Space Models (SSMs), particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design View-Fused Mamba (VFM) with a Mamba-Transformer-style architecture to efficiently fuse information from different views with a selective state mechanism. We also design Spectrum-Guided Mamba (SGM) with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refining process. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-F/GLADMamba.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 02:40:17 GMT" } ]
2025-03-25T00:00:00
[ [ "Fu", "Yali", "" ], [ "Li", "Jindong", "" ], [ "Wang", "Qi", "" ], [ "Xing", "Qianli", "" ] ]
TITLE: GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model ABSTRACT: Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture the long-range dependencies efficiently and neglect the spectral information. Recently, selective State Space Models (SSMs), particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design View-Fused Mamba (VFM) with a Mamba-Transformer-style architecture to efficiently fuse information from different views with a selective state mechanism. We also design Spectrum-Guided Mamba (SGM) with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refining process. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-F/GLADMamba.
2503.17905
Luke McDermott
Luke McDermott, Rahul Parhi
Finding Stable Subnetworks at Initialization with Dataset Distillation
null
null
null
null
cs.LG
http://creativecommons.org/licenses/by/4.0/
Recent works have shown that Dataset Distillation, the process for summarizing the training data, can be leveraged to accelerate the training of deep learning models. However, its impact on training dynamics, particularly in neural network pruning, remains largely unexplored. In our work, we use distilled data in the inner loop of iterative magnitude pruning to produce sparse, trainable subnetworks at initialization -- more commonly known as lottery tickets. While using 150x less training points, our algorithm matches the performance of traditional lottery ticket rewinding on ResNet-18 & CIFAR-10. Previous work highlights that lottery tickets can be found when the dense initialization is stable to SGD noise (i.e. training across different ordering of the data converges to the same minima). We extend this discovery, demonstrating that stable subnetworks can exist even within an unstable dense initialization. In our linear mode connectivity studies, we find that pruning with distilled data discards parameters that contribute to the sharpness of the loss landscape. Lastly, we show that by first generating a stable sparsity mask at initialization, we can find lottery tickets at significantly higher sparsities than traditional iterative magnitude pruning.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 02:55:57 GMT" } ]
2025-03-25T00:00:00
[ [ "McDermott", "Luke", "" ], [ "Parhi", "Rahul", "" ] ]
TITLE: Finding Stable Subnetworks at Initialization with Dataset Distillation ABSTRACT: Recent works have shown that Dataset Distillation, the process for summarizing the training data, can be leveraged to accelerate the training of deep learning models. However, its impact on training dynamics, particularly in neural network pruning, remains largely unexplored. In our work, we use distilled data in the inner loop of iterative magnitude pruning to produce sparse, trainable subnetworks at initialization -- more commonly known as lottery tickets. While using 150x less training points, our algorithm matches the performance of traditional lottery ticket rewinding on ResNet-18 & CIFAR-10. Previous work highlights that lottery tickets can be found when the dense initialization is stable to SGD noise (i.e. training across different ordering of the data converges to the same minima). We extend this discovery, demonstrating that stable subnetworks can exist even within an unstable dense initialization. In our linear mode connectivity studies, we find that pruning with distilled data discards parameters that contribute to the sharpness of the loss landscape. Lastly, we show that by first generating a stable sparsity mask at initialization, we can find lottery tickets at significantly higher sparsities than traditional iterative magnitude pruning.
2503.17911
Peng Cheng
Xiaoyao Zhong, Haotian Li, Jiabao Jin, Mingyu Yang, Deming Chu, Xiangyu Wang, Zhitao Shen, Wei Jia, George Gu, Yi Xie, Xuemin Lin, Heng Tao Shen, Jingkuan Song, Peng Cheng
VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Search
16 pages, the report of open-source library VSAG (https://github.com/antgroup/vsag)
null
null
null
cs.DB
http://creativecommons.org/licenses/by/4.0/
Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accuracy with practical efficiency. Despite the advancements, these algorithms still face performance bottlenecks in production, due to the random memory access patterns of graph-based search and the high computational overheads of vector distance. In addition, the performance of a graph-based ANNS algorithm is highly sensitive to parameters, while selecting the optimal parameters is cost-prohibitive, e.g., manual tuning requires repeatedly re-building the index. This paper introduces VSAG, an open-source framework that aims to enhance the in production performance of graph-based ANNS algorithms. VSAG has been deployed at scale in the services of Ant Group, and it incorporates three key optimizations: (i) efficient memory access: it reduces L3 cache misses with pre-fetching and cache-friendly vector organization; (ii) automated parameter tuning: it automatically selects performance-optimal parameters without requiring index rebuilding; (iii) efficient distance computation: it leverages modern hardware, scalar quantization, and smartly switches to low-precision representation to dramatically reduce the distance computation costs. We evaluate VSAG on real-world datasets. The experimental results show that VSAG achieves the state-of-the-art performance and provides up to 4x speedup over HNSWlib (an industry-standard library) while ensuring the same accuracy.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 03:16:50 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhong", "Xiaoyao", "" ], [ "Li", "Haotian", "" ], [ "Jin", "Jiabao", "" ], [ "Yang", "Mingyu", "" ], [ "Chu", "Deming", "" ], [ "Wang", "Xiangyu", "" ], [ "Shen", "Zhitao", "" ], [ "Jia", "Wei", "" ], [ "Gu", "George", "" ], [ "Xie", "Yi", "" ], [ "Lin", "Xuemin", "" ], [ "Shen", "Heng Tao", "" ], [ "Song", "Jingkuan", "" ], [ "Cheng", "Peng", "" ] ]
TITLE: VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Search ABSTRACT: Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accuracy with practical efficiency. Despite the advancements, these algorithms still face performance bottlenecks in production, due to the random memory access patterns of graph-based search and the high computational overheads of vector distance. In addition, the performance of a graph-based ANNS algorithm is highly sensitive to parameters, while selecting the optimal parameters is cost-prohibitive, e.g., manual tuning requires repeatedly re-building the index. This paper introduces VSAG, an open-source framework that aims to enhance the in production performance of graph-based ANNS algorithms. VSAG has been deployed at scale in the services of Ant Group, and it incorporates three key optimizations: (i) efficient memory access: it reduces L3 cache misses with pre-fetching and cache-friendly vector organization; (ii) automated parameter tuning: it automatically selects performance-optimal parameters without requiring index rebuilding; (iii) efficient distance computation: it leverages modern hardware, scalar quantization, and smartly switches to low-precision representation to dramatically reduce the distance computation costs. We evaluate VSAG on real-world datasets. The experimental results show that VSAG achieves the state-of-the-art performance and provides up to 4x speedup over HNSWlib (an industry-standard library) while ensuring the same accuracy.
2503.17914
Yazhou Yao
Jianjian Yin, Tao Chen, Gensheng Pei, Yazhou Yao, Liqiang Nie, Xiansheng Hua
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning
accepted by IEEE Transactions on Multimedia
null
null
null
cs.MM cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Consistency regularization has prevailed in semi-supervised semantic segmentation and achieved promising performance. However, existing methods typically concentrate on enhancing the Image-augmentation based Prediction consistency and optimizing the segmentation network as a whole, resulting in insufficient utilization of potential supervisory information. In this paper, we propose a Multi-Constraint Consistency Learning (MCCL) approach to facilitate the staged enhancement of the encoder and decoder. Specifically, we first design a feature knowledge alignment (FKA) strategy to promote the feature consistency learning of the encoder from image-augmentation. Our FKA encourages the encoder to derive consistent features for strongly and weakly augmented views from the perspectives of point-to-point alignment and prototype-based intra-class compactness. Moreover, we propose a self-adaptive intervention (SAI) module to increase the discrepancy of aligned intermediate feature representations, promoting Feature-perturbation based Prediction consistency learning. Self-adaptive feature masking and noise injection are designed in an instance-specific manner to perturb the features for robust learning of the decoder. Experimental results on Pascal VOC2012 and Cityscapes datasets demonstrate that our proposed MCCL achieves new state-of-the-art performance. The source code and models are made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MCCL.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 03:21:33 GMT" } ]
2025-03-25T00:00:00
[ [ "Yin", "Jianjian", "" ], [ "Chen", "Tao", "" ], [ "Pei", "Gensheng", "" ], [ "Yao", "Yazhou", "" ], [ "Nie", "Liqiang", "" ], [ "Hua", "Xiansheng", "" ] ]
TITLE: Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning ABSTRACT: Consistency regularization has prevailed in semi-supervised semantic segmentation and achieved promising performance. However, existing methods typically concentrate on enhancing the Image-augmentation based Prediction consistency and optimizing the segmentation network as a whole, resulting in insufficient utilization of potential supervisory information. In this paper, we propose a Multi-Constraint Consistency Learning (MCCL) approach to facilitate the staged enhancement of the encoder and decoder. Specifically, we first design a feature knowledge alignment (FKA) strategy to promote the feature consistency learning of the encoder from image-augmentation. Our FKA encourages the encoder to derive consistent features for strongly and weakly augmented views from the perspectives of point-to-point alignment and prototype-based intra-class compactness. Moreover, we propose a self-adaptive intervention (SAI) module to increase the discrepancy of aligned intermediate feature representations, promoting Feature-perturbation based Prediction consistency learning. Self-adaptive feature masking and noise injection are designed in an instance-specific manner to perturb the features for robust learning of the decoder. Experimental results on Pascal VOC2012 and Cityscapes datasets demonstrate that our proposed MCCL achieves new state-of-the-art performance. The source code and models are made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MCCL.
2503.17928
Zefeng Zhang
Zefeng Zhang, Hengzhu Tang, Jiawei Sheng, Zhenyu Zhang, Yiming Ren, Zhenyang Li, Dawei Yin, Duohe Ma, Tingwen Liu
Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization
CVPR 2025
null
null
null
cs.CV cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multimodal Large Language Models excel in various tasks, yet often struggle with modality bias, where the model tends to rely heavily on a single modality and overlook critical information in other modalities, which leads to incorrect focus and generating irrelevant responses. In this paper, we propose using the paradigm of preference optimization to solve the modality bias problem, including RLAIFVBias, a debiased preference optimization dataset, and a Noise Aware Preference Optimization algorithm. Specifically, we first construct the dataset by introducing perturbations to reduce the informational content of certain modalities, compelling the model to rely on a specific modality when generating negative responses. To address the inevitable noise in automatically constructed data, we combine the noise robust Mean Absolute Error with the Binary Cross Entropy in Direct Preference Optimization by a negative Box Cox transformation, and dynamically adjust the algorithm noise robustness based on the evaluated noise levels in the data. Extensive experiments validate our approach, demonstrating not only its effectiveness in mitigating modality bias but also its significant role in minimizing hallucinations.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 04:00:11 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhang", "Zefeng", "" ], [ "Tang", "Hengzhu", "" ], [ "Sheng", "Jiawei", "" ], [ "Zhang", "Zhenyu", "" ], [ "Ren", "Yiming", "" ], [ "Li", "Zhenyang", "" ], [ "Yin", "Dawei", "" ], [ "Ma", "Duohe", "" ], [ "Liu", "Tingwen", "" ] ]
TITLE: Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization ABSTRACT: Multimodal Large Language Models excel in various tasks, yet often struggle with modality bias, where the model tends to rely heavily on a single modality and overlook critical information in other modalities, which leads to incorrect focus and generating irrelevant responses. In this paper, we propose using the paradigm of preference optimization to solve the modality bias problem, including RLAIFVBias, a debiased preference optimization dataset, and a Noise Aware Preference Optimization algorithm. Specifically, we first construct the dataset by introducing perturbations to reduce the informational content of certain modalities, compelling the model to rely on a specific modality when generating negative responses. To address the inevitable noise in automatically constructed data, we combine the noise robust Mean Absolute Error with the Binary Cross Entropy in Direct Preference Optimization by a negative Box Cox transformation, and dynamically adjust the algorithm noise robustness based on the evaluated noise levels in the data. Extensive experiments validate our approach, demonstrating not only its effectiveness in mitigating modality bias but also its significant role in minimizing hallucinations.
2503.17933
Zhengyi Ou
Justice Ou, Tinglin Huang, Yilun Zhao, Ziyang Yu, Peiqing Lu, Rex Ying
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA
null
null
null
null
cs.CL cs.AI cs.IR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context grounded in real-world patient experiences. Motivated by this, we propose Experience Retrieval Augmentation - ExpRAG framework based on Electronic Health Record (EHR), aiming to offer the relevant context from other patients' discharge reports. ExpRAG performs retrieval through a coarse-to-fine process, utilizing an EHR-based report ranker to efficiently identify similar patients, followed by an experience retriever to extract task-relevant content for enhanced medical reasoning. To evaluate ExpRAG, we introduce DischargeQA, a clinical QA dataset with 1,280 discharge-related questions across diagnosis, medication, and instruction tasks. Each problem is generated using EHR data to ensure realistic and challenging scenarios. Experimental results demonstrate that ExpRAG consistently outperforms a text-based ranker, achieving an average relative improvement of 5.2%, highlighting the importance of case-based knowledge for medical reasoning.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 04:26:06 GMT" } ]
2025-03-25T00:00:00
[ [ "Ou", "Justice", "" ], [ "Huang", "Tinglin", "" ], [ "Zhao", "Yilun", "" ], [ "Yu", "Ziyang", "" ], [ "Lu", "Peiqing", "" ], [ "Ying", "Rex", "" ] ]
TITLE: Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA ABSTRACT: To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context grounded in real-world patient experiences. Motivated by this, we propose Experience Retrieval Augmentation - ExpRAG framework based on Electronic Health Record (EHR), aiming to offer the relevant context from other patients' discharge reports. ExpRAG performs retrieval through a coarse-to-fine process, utilizing an EHR-based report ranker to efficiently identify similar patients, followed by an experience retriever to extract task-relevant content for enhanced medical reasoning. To evaluate ExpRAG, we introduce DischargeQA, a clinical QA dataset with 1,280 discharge-related questions across diagnosis, medication, and instruction tasks. Each problem is generated using EHR data to ensure realistic and challenging scenarios. Experimental results demonstrate that ExpRAG consistently outperforms a text-based ranker, achieving an average relative improvement of 5.2%, highlighting the importance of case-based knowledge for medical reasoning.
2503.17934
Zhimin Chen
Xuewei Chen, Zhimin Chen, Yiren Song
TransAnimate: Taming Layer Diffusion to Generate RGBA Video
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Text-to-video generative models have made remarkable advancements in recent years. However, generating RGBA videos with alpha channels for transparency and visual effects remains a significant challenge due to the scarcity of suitable datasets and the complexity of adapting existing models for this purpose. To address these limitations, we present TransAnimate, an innovative framework that integrates RGBA image generation techniques with video generation modules, enabling the creation of dynamic and transparent videos. TransAnimate efficiently leverages pre-trained text-to-transparent image model weights and combines them with temporal models and controllability plugins trained on RGB videos, adapting them for controllable RGBA video generation tasks. Additionally, we introduce an interactive motion-guided control mechanism, where directional arrows define movement and colors adjust scaling, offering precise and intuitive control for designing game effects. To further alleviate data scarcity, we have developed a pipeline for creating an RGBA video dataset, incorporating high-quality game effect videos, extracted foreground objects, and synthetic transparent videos. Comprehensive experiments demonstrate that TransAnimate generates high-quality RGBA videos, establishing it as a practical and effective tool for applications in gaming and visual effects.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 04:27:46 GMT" } ]
2025-03-25T00:00:00
[ [ "Chen", "Xuewei", "" ], [ "Chen", "Zhimin", "" ], [ "Song", "Yiren", "" ] ]
TITLE: TransAnimate: Taming Layer Diffusion to Generate RGBA Video ABSTRACT: Text-to-video generative models have made remarkable advancements in recent years. However, generating RGBA videos with alpha channels for transparency and visual effects remains a significant challenge due to the scarcity of suitable datasets and the complexity of adapting existing models for this purpose. To address these limitations, we present TransAnimate, an innovative framework that integrates RGBA image generation techniques with video generation modules, enabling the creation of dynamic and transparent videos. TransAnimate efficiently leverages pre-trained text-to-transparent image model weights and combines them with temporal models and controllability plugins trained on RGB videos, adapting them for controllable RGBA video generation tasks. Additionally, we introduce an interactive motion-guided control mechanism, where directional arrows define movement and colors adjust scaling, offering precise and intuitive control for designing game effects. To further alleviate data scarcity, we have developed a pipeline for creating an RGBA video dataset, incorporating high-quality game effect videos, extracted foreground objects, and synthetic transparent videos. Comprehensive experiments demonstrate that TransAnimate generates high-quality RGBA videos, establishing it as a practical and effective tool for applications in gaming and visual effects.
2503.17936
Riya Naik
Riya Naik, Ashwin Srinivasan, Estrid He, and Swati Agarwal
An Empirical Study of the Role of Incompleteness and Ambiguity in Interactions with Large Language Models
null
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Natural language as a medium for human-computer interaction has long been anticipated, has been undergoing a sea-change with the advent of Large Language Models (LLMs) with startling capacities for processing and generating language. Many of us now treat LLMs as modern-day oracles, asking it almost any kind of question. Unlike its Delphic predecessor, consulting an LLM does not have to be a single-turn activity (ask a question, receive an answer, leave); and -- also unlike the Pythia -- it is widely acknowledged that answers from LLMs can be improved with additional context. In this paper, we aim to study when we need multi-turn interactions with LLMs to successfully get a question answered; or conclude that a question is unanswerable. We present a neural symbolic framework that models the interactions between human and LLM agents. Through the proposed framework, we define incompleteness and ambiguity in the questions as properties deducible from the messages exchanged in the interaction, and provide results from benchmark problems, in which the answer-correctness is shown to depend on whether or not questions demonstrate the presence of incompleteness or ambiguity (according to the properties we identify). Our results show multi-turn interactions are usually required for datasets which have a high proportion of incompleteness or ambiguous questions; and that that increasing interaction length has the effect of reducing incompleteness or ambiguity. The results also suggest that our measures of incompleteness and ambiguity can be useful tools for characterising interactions with an LLM on question-answeringproblems
[ { "version": "v1", "created": "Sun, 23 Mar 2025 04:34:30 GMT" } ]
2025-03-25T00:00:00
[ [ "Naik", "Riya", "" ], [ "Srinivasan", "Ashwin", "" ], [ "He", "Estrid", "" ], [ "Agarwal", "Swati", "" ] ]
TITLE: An Empirical Study of the Role of Incompleteness and Ambiguity in Interactions with Large Language Models ABSTRACT: Natural language as a medium for human-computer interaction has long been anticipated, has been undergoing a sea-change with the advent of Large Language Models (LLMs) with startling capacities for processing and generating language. Many of us now treat LLMs as modern-day oracles, asking it almost any kind of question. Unlike its Delphic predecessor, consulting an LLM does not have to be a single-turn activity (ask a question, receive an answer, leave); and -- also unlike the Pythia -- it is widely acknowledged that answers from LLMs can be improved with additional context. In this paper, we aim to study when we need multi-turn interactions with LLMs to successfully get a question answered; or conclude that a question is unanswerable. We present a neural symbolic framework that models the interactions between human and LLM agents. Through the proposed framework, we define incompleteness and ambiguity in the questions as properties deducible from the messages exchanged in the interaction, and provide results from benchmark problems, in which the answer-correctness is shown to depend on whether or not questions demonstrate the presence of incompleteness or ambiguity (according to the properties we identify). Our results show multi-turn interactions are usually required for datasets which have a high proportion of incompleteness or ambiguous questions; and that that increasing interaction length has the effect of reducing incompleteness or ambiguity. The results also suggest that our measures of incompleteness and ambiguity can be useful tools for characterising interactions with an LLM on question-answeringproblems
2503.17937
Zhi Zhang
Zhi Zhang, Daoyi Chen
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Single underwater image enhancement (UIE) is a challenging ill-posed problem, but its development is hindered by two major issues: (1) The labels in underwater reference datasets are pseudo labels, relying on these pseudo ground truths in supervised learning leads to domain discrepancy. (2) Underwater reference datasets are scarce, making training on such small datasets prone to overfitting and distribution shift. To address these challenges, we propose Trans-UIE, a transfer learning-based UIE model that captures the fundamental paradigms of UIE through pretraining and utilizes a dataset composed of both reference and non-reference datasets for fine-tuning. However, fine-tuning the model using only reconstruction loss may introduce confirmation bias. To mitigate this, our method leverages no-reference image quality assessment (NR-IQA) metrics from above-water scenes to guide the transfer learning process across domains while generating enhanced images with the style of the above-water image domain. Additionally, to reduce the risk of overfitting during the pretraining stage, we introduce Pearson correlation loss. Experimental results on both full-reference and no-reference underwater benchmark datasets demonstrate that Trans-UIE significantly outperforms state-of-the-art methods.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 04:40:07 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhang", "Zhi", "" ], [ "Chen", "Daoyi", "" ] ]
TITLE: Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach ABSTRACT: Single underwater image enhancement (UIE) is a challenging ill-posed problem, but its development is hindered by two major issues: (1) The labels in underwater reference datasets are pseudo labels, relying on these pseudo ground truths in supervised learning leads to domain discrepancy. (2) Underwater reference datasets are scarce, making training on such small datasets prone to overfitting and distribution shift. To address these challenges, we propose Trans-UIE, a transfer learning-based UIE model that captures the fundamental paradigms of UIE through pretraining and utilizes a dataset composed of both reference and non-reference datasets for fine-tuning. However, fine-tuning the model using only reconstruction loss may introduce confirmation bias. To mitigate this, our method leverages no-reference image quality assessment (NR-IQA) metrics from above-water scenes to guide the transfer learning process across domains while generating enhanced images with the style of the above-water image domain. Additionally, to reduce the risk of overfitting during the pretraining stage, we introduce Pearson correlation loss. Experimental results on both full-reference and no-reference underwater benchmark datasets demonstrate that Trans-UIE significantly outperforms state-of-the-art methods.
2503.17941
Sunwoong Yang
Sunwoong Yang, Youngkyu Lee, Namwoo Kang
Physics-Guided Multi-Fidelity DeepONet for Data-Efficient Flow Field Prediction
null
null
null
null
physics.flu-dyn cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This study presents an enhanced multi-fidelity deep operator network (DeepONet) framework for efficient spatio-temporal flow field prediction, with particular emphasis on practical scenarios where high-fidelity data is scarce. We introduce several key innovations to improve the framework's efficiency and accuracy. First, we enhance the DeepONet architecture by incorporating a merge network that enables more complex feature interactions between operator and coordinate spaces, achieving a 50.4% reduction in prediction error compared to traditional dot-product operations. We further optimize the architecture through temporal positional encoding and point-based sampling strategies, achieving a 7.57% improvement in prediction accuracy while reducing training time by 96% through efficient sampling and automatic mixed precision training. Building upon this foundation, we develop a transfer learning-based multi-fidelity framework that leverages knowledge from pre-trained low-fidelity models to guide high-fidelity predictions. Our approach freezes the pre-trained branch and trunk networks while making only the merge network trainable during high-fidelity training, preserving valuable low-fidelity representations while efficiently adapting to high-fidelity features. Through systematic investigation, we demonstrate that this fine-tuning strategy not only significantly outperforms linear probing and full-tuning alternatives but also surpasses conventional multi-fidelity frameworks by up to 76%, while achieving up to 43.7% improvement in prediction accuracy compared to single-fidelity training. The core contribution lies in our novel time-derivative guided sampling approach: it maintains prediction accuracy equivalent to models trained with the full dataset while requiring only 60% of the original high-fidelity samples.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 04:48:18 GMT" } ]
2025-03-25T00:00:00
[ [ "Yang", "Sunwoong", "" ], [ "Lee", "Youngkyu", "" ], [ "Kang", "Namwoo", "" ] ]
TITLE: Physics-Guided Multi-Fidelity DeepONet for Data-Efficient Flow Field Prediction ABSTRACT: This study presents an enhanced multi-fidelity deep operator network (DeepONet) framework for efficient spatio-temporal flow field prediction, with particular emphasis on practical scenarios where high-fidelity data is scarce. We introduce several key innovations to improve the framework's efficiency and accuracy. First, we enhance the DeepONet architecture by incorporating a merge network that enables more complex feature interactions between operator and coordinate spaces, achieving a 50.4% reduction in prediction error compared to traditional dot-product operations. We further optimize the architecture through temporal positional encoding and point-based sampling strategies, achieving a 7.57% improvement in prediction accuracy while reducing training time by 96% through efficient sampling and automatic mixed precision training. Building upon this foundation, we develop a transfer learning-based multi-fidelity framework that leverages knowledge from pre-trained low-fidelity models to guide high-fidelity predictions. Our approach freezes the pre-trained branch and trunk networks while making only the merge network trainable during high-fidelity training, preserving valuable low-fidelity representations while efficiently adapting to high-fidelity features. Through systematic investigation, we demonstrate that this fine-tuning strategy not only significantly outperforms linear probing and full-tuning alternatives but also surpasses conventional multi-fidelity frameworks by up to 76%, while achieving up to 43.7% improvement in prediction accuracy compared to single-fidelity training. The core contribution lies in our novel time-derivative guided sampling approach: it maintains prediction accuracy equivalent to models trained with the full dataset while requiring only 60% of the original high-fidelity samples.
2503.17949
Zeeshan Ahmad
Moin Uddin Maruf and Sungmin Kim and Zeeshan Ahmad
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
24 pages, 5 figures, 1 table + 12 pages of Supporting Information
null
null
null
physics.chem-ph cond-mat.mtrl-sci cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 05:26:55 GMT" } ]
2025-03-25T00:00:00
[ [ "Maruf", "Moin Uddin", "" ], [ "Kim", "Sungmin", "" ], [ "Ahmad", "Zeeshan", "" ] ]
TITLE: Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution ABSTRACT: Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science.
2503.17956
Sami Zhioua
Sami Zhioua, Ruta Binkyte, Ayoub Ouni, Farah Barika Ktata
On the Origins of Sampling Bias: Implications on Fairness Measurement and Mitigation
null
null
null
null
cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplification or underestimation of the existing disparity. Several sources of bias exist and it is assumed that bias resulting from machine learning is born equally by different groups (e.g. females vs males, whites vs blacks, etc.). If, however, bias is born differently by different groups, it may exacerbate discrimination against specific sub-populations. Sampling bias, in particular, is inconsistently used in the literature to describe bias due to the sampling procedure. In this paper, we attempt to disambiguate this term by introducing clearly defined variants of sampling bias, namely, sample size bias (SSB) and underrepresentation bias (URB). Through an extensive set of experiments on benchmark datasets and using mainstream learning algorithms, we expose relevant observations in several model training scenarios. The observations are finally framed as actionable recommendations for practitioners.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 06:23:07 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhioua", "Sami", "" ], [ "Binkyte", "Ruta", "" ], [ "Ouni", "Ayoub", "" ], [ "Ktata", "Farah Barika", "" ] ]
TITLE: On the Origins of Sampling Bias: Implications on Fairness Measurement and Mitigation ABSTRACT: Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplification or underestimation of the existing disparity. Several sources of bias exist and it is assumed that bias resulting from machine learning is born equally by different groups (e.g. females vs males, whites vs blacks, etc.). If, however, bias is born differently by different groups, it may exacerbate discrimination against specific sub-populations. Sampling bias, in particular, is inconsistently used in the literature to describe bias due to the sampling procedure. In this paper, we attempt to disambiguate this term by introducing clearly defined variants of sampling bias, namely, sample size bias (SSB) and underrepresentation bias (URB). Through an extensive set of experiments on benchmark datasets and using mainstream learning algorithms, we expose relevant observations in several model training scenarios. The observations are finally framed as actionable recommendations for practitioners.
2503.17963
Hyunwoo Ko
Guijin Son, Hyunwoo Ko, Haneral Jung, Chami Hwang
Won: Establishing Best Practices for Korean Financial NLP
The training dataset is uploaded here: https://huggingface.co/datasets/KRX-Data/Won-Instruct. The model will be updated shortly
null
null
null
cs.CL
http://creativecommons.org/licenses/by-nc-sa/4.0/
In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for about eight weeks, the leaderboard evaluated 1,119 submissions on a closed benchmark covering five MCQA categories: finance and accounting, stock price prediction, domestic company analysis, financial markets, and financial agent tasks and one open-ended qa task. Building on insights from these evaluations, we release an open instruction dataset of 80k instances and summarize widely used training strategies observed among top-performing models. Finally, we introduce Won, a fully open and transparent LLM built using these best practices. We hope our contributions help advance the development of better and safer financial LLMs for Korean and other languages.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 06:52:38 GMT" } ]
2025-03-25T00:00:00
[ [ "Son", "Guijin", "" ], [ "Ko", "Hyunwoo", "" ], [ "Jung", "Haneral", "" ], [ "Hwang", "Chami", "" ] ]
TITLE: Won: Establishing Best Practices for Korean Financial NLP ABSTRACT: In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for about eight weeks, the leaderboard evaluated 1,119 submissions on a closed benchmark covering five MCQA categories: finance and accounting, stock price prediction, domestic company analysis, financial markets, and financial agent tasks and one open-ended qa task. Building on insights from these evaluations, we release an open instruction dataset of 80k instances and summarize widely used training strategies observed among top-performing models. Finally, we introduce Won, a fully open and transparent LLM built using these best practices. We hope our contributions help advance the development of better and safer financial LLMs for Korean and other languages.
2503.17966
Wei Lu
Zeng-Hui Zhu, Wei Lu, Si-Bao Chen, Chris H. Q. Ding, Jin Tang, and Bin Luo
Real-World Remote Sensing Image Dehazing: Benchmark and Baseline
11 pages, 9 figures, real-world remote sensing image dehazing dataset
null
null
null
cs.CV eess.IV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality. The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets. However, these methods struggle with real-world applications due to the inherent domain gap between synthetic and real data. To address this, we introduce Real-World Remote Sensing Hazy Image Dataset (RRSHID), the first large-scale dataset featuring real-world hazy and dehazed image pairs across diverse atmospheric conditions. Based on this, we propose MCAF-Net, a novel framework tailored for real-world RSID. Its effectiveness arises from three innovative components: Multi-branch Feature Integration Block Aggregator (MFIBA), which enables robust feature extraction through cascaded integration blocks and parallel multi-branch processing; Color-Calibrated Self-Supervised Attention Module (CSAM), which mitigates complex color distortions via self-supervised learning and attention-guided refinement; and Multi-Scale Feature Adaptive Fusion Module (MFAFM), which integrates features effectively while preserving local details and global context. Extensive experiments validate that MCAF-Net demonstrates state-of-the-art performance in real-world RSID, while maintaining competitive performance on synthetic datasets. The introduction of RRSHID and MCAF-Net sets new benchmarks for real-world RSID research, advancing practical solutions for this complex task. The code and dataset are publicly available at \url{https://github.com/lwCVer/RRSHID}.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 07:15:46 GMT" } ]
2025-03-25T00:00:00
[ [ "Zhu", "Zeng-Hui", "" ], [ "Lu", "Wei", "" ], [ "Chen", "Si-Bao", "" ], [ "Ding", "Chris H. Q.", "" ], [ "Tang", "Jin", "" ], [ "Luo", "Bin", "" ] ]
TITLE: Real-World Remote Sensing Image Dehazing: Benchmark and Baseline ABSTRACT: Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality. The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets. However, these methods struggle with real-world applications due to the inherent domain gap between synthetic and real data. To address this, we introduce Real-World Remote Sensing Hazy Image Dataset (RRSHID), the first large-scale dataset featuring real-world hazy and dehazed image pairs across diverse atmospheric conditions. Based on this, we propose MCAF-Net, a novel framework tailored for real-world RSID. Its effectiveness arises from three innovative components: Multi-branch Feature Integration Block Aggregator (MFIBA), which enables robust feature extraction through cascaded integration blocks and parallel multi-branch processing; Color-Calibrated Self-Supervised Attention Module (CSAM), which mitigates complex color distortions via self-supervised learning and attention-guided refinement; and Multi-Scale Feature Adaptive Fusion Module (MFAFM), which integrates features effectively while preserving local details and global context. Extensive experiments validate that MCAF-Net demonstrates state-of-the-art performance in real-world RSID, while maintaining competitive performance on synthetic datasets. The introduction of RRSHID and MCAF-Net sets new benchmarks for real-world RSID research, advancing practical solutions for this complex task. The code and dataset are publicly available at \url{https://github.com/lwCVer/RRSHID}.
2503.17978
Dominique Nshimyimana
Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh, Bo Zhou, Paul Lukowicz
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-supervised learning (SSL) has emerged as an effective approach to leverage unlabeled data through pretext tasks, such as masked reconstruction and multitask learning with signal processing-based data augmentations, to pre-train encoder models. However, such methods are often derived from computer vision approaches that disregard physical mechanisms and constraints that govern wearable sensor data and the phenomena they reflect. In this paper, we propose a physics-informed multi-task pre-training (PIM) framework for IMU-based HAR. PIM generates pre-text tasks based on the understanding of basic physical aspects of human motion: including movement speed, angles of movement, and symmetry between sensor placements. Given a sensor signal, we calculate corresponding features using physics-based equations and use them as pretext tasks for SSL. This enables the model to capture fundamental physical characteristics of human activities, which is especially relevant for multi-sensor systems. Experimental evaluations on four HAR benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art methods, including data augmentation and masked reconstruction, in terms of accuracy and F1 score. We have observed gains of almost 10\% in macro f1 score and accuracy with only 2 to 8 labeled examples per class and up to 3% when there is no reduction in the amount of training data.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 08:16:01 GMT" } ]
2025-03-25T00:00:00
[ [ "Nshimyimana", "Dominique", "" ], [ "Rey", "Vitor Fortes", "" ], [ "Suh", "Sungho", "" ], [ "Zhou", "Bo", "" ], [ "Lukowicz", "Paul", "" ] ]
TITLE: PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition ABSTRACT: Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-supervised learning (SSL) has emerged as an effective approach to leverage unlabeled data through pretext tasks, such as masked reconstruction and multitask learning with signal processing-based data augmentations, to pre-train encoder models. However, such methods are often derived from computer vision approaches that disregard physical mechanisms and constraints that govern wearable sensor data and the phenomena they reflect. In this paper, we propose a physics-informed multi-task pre-training (PIM) framework for IMU-based HAR. PIM generates pre-text tasks based on the understanding of basic physical aspects of human motion: including movement speed, angles of movement, and symmetry between sensor placements. Given a sensor signal, we calculate corresponding features using physics-based equations and use them as pretext tasks for SSL. This enables the model to capture fundamental physical characteristics of human activities, which is especially relevant for multi-sensor systems. Experimental evaluations on four HAR benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art methods, including data augmentation and masked reconstruction, in terms of accuracy and F1 score. We have observed gains of almost 10\% in macro f1 score and accuracy with only 2 to 8 labeled examples per class and up to 3% when there is no reduction in the amount of training data.
2503.17982
Yara AlaaEldin
Yara AlaaEldin and Francesca Odone
Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
Understanding the geometric and semantic properties of the scene is crucial in autonomous navigation and particularly challenging in the case of Unmanned Aerial Vehicle (UAV) navigation. Such information may be by obtained by estimating depth and semantic segmentation maps of the surrounding environment and for their practical use in autonomous navigation, the procedure must be performed as close to real-time as possible. In this paper, we leverage monocular cameras on aerial robots to predict depth and semantic maps in low-altitude unstructured environments. We propose a joint deep-learning architecture that can perform the two tasks accurately and rapidly, and validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our joint-architecture proves to be competitive or superior to the other single and joint architecture methods while performing its task fast predicting 20.2 FPS on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All codes for training and prediction can be found on this link: https://github.com/Malga-Vision/Co-SemDepth
[ { "version": "v1", "created": "Sun, 23 Mar 2025 08:25:07 GMT" } ]
2025-03-25T00:00:00
[ [ "AlaaEldin", "Yara", "" ], [ "Odone", "Francesca", "" ] ]
TITLE: Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images ABSTRACT: Understanding the geometric and semantic properties of the scene is crucial in autonomous navigation and particularly challenging in the case of Unmanned Aerial Vehicle (UAV) navigation. Such information may be by obtained by estimating depth and semantic segmentation maps of the surrounding environment and for their practical use in autonomous navigation, the procedure must be performed as close to real-time as possible. In this paper, we leverage monocular cameras on aerial robots to predict depth and semantic maps in low-altitude unstructured environments. We propose a joint deep-learning architecture that can perform the two tasks accurately and rapidly, and validate its effectiveness on MidAir and Aeroscapes benchmark datasets. Our joint-architecture proves to be competitive or superior to the other single and joint architecture methods while performing its task fast predicting 20.2 FPS on a single NVIDIA quadro p5000 GPU and it has a low memory footprint. All codes for training and prediction can be found on this link: https://github.com/Malga-Vision/Co-SemDepth
2503.17984
Maochen Yang
Maochen Yang, Zekun Li, Jian Zhang, Lei Qi, Yinghuan Shi
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
Accepted by CVPR 2025
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on https://github.com/syhien/taste_more_taste_better.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 08:38:01 GMT" } ]
2025-03-25T00:00:00
[ [ "Yang", "Maochen", "" ], [ "Li", "Zekun", "" ], [ "Zhang", "Jian", "" ], [ "Qi", "Lei", "" ], [ "Shi", "Yinghuan", "" ] ]
TITLE: Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting ABSTRACT: Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on https://github.com/syhien/taste_more_taste_better.
2503.17990
Venktesh V
V Venktesh, Mandeep Rathee, Avishek Anand
SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA
Accepted at NAACL 2025 Main Conference
null
null
null
cs.IR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Complex question-answering (QA) systems face significant challenges in retrieving and reasoning over information that addresses multi-faceted queries. While large language models (LLMs) have advanced the reasoning capabilities of these systems, the bounded-recall problem persists, where procuring all relevant documents in first-stage retrieval remains a challenge. Missing pertinent documents at this stage leads to performance degradation that cannot be remedied in later stages, especially given the limited context windows of LLMs which necessitate high recall at smaller retrieval depths. In this paper, we introduce SUNAR, a novel approach that leverages LLMs to guide a Neighborhood Aware Retrieval process. SUNAR iteratively explores a neighborhood graph of documents, dynamically promoting or penalizing documents based on uncertainty estimates from interim LLM-generated answer candidates. We validate our approach through extensive experiments on two complex QA datasets. Our results show that SUNAR significantly outperforms existing retrieve-and-reason baselines, achieving up to a 31.84% improvement in performance over existing state-of-the-art methods for complex QA.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 08:50:44 GMT" } ]
2025-03-25T00:00:00
[ [ "Venktesh", "V", "" ], [ "Rathee", "Mandeep", "" ], [ "Anand", "Avishek", "" ] ]
TITLE: SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA ABSTRACT: Complex question-answering (QA) systems face significant challenges in retrieving and reasoning over information that addresses multi-faceted queries. While large language models (LLMs) have advanced the reasoning capabilities of these systems, the bounded-recall problem persists, where procuring all relevant documents in first-stage retrieval remains a challenge. Missing pertinent documents at this stage leads to performance degradation that cannot be remedied in later stages, especially given the limited context windows of LLMs which necessitate high recall at smaller retrieval depths. In this paper, we introduce SUNAR, a novel approach that leverages LLMs to guide a Neighborhood Aware Retrieval process. SUNAR iteratively explores a neighborhood graph of documents, dynamically promoting or penalizing documents based on uncertainty estimates from interim LLM-generated answer candidates. We validate our approach through extensive experiments on two complex QA datasets. Our results show that SUNAR significantly outperforms existing retrieve-and-reason baselines, achieving up to a 31.84% improvement in performance over existing state-of-the-art methods for complex QA.
2503.17992
Yuping Duan
Xueying Liu, Lianfang Wang, Jun Liu, Yong Wang and Yuping Duan
Geometric Constrained Non-Line-of-Sight Imaging
null
null
null
null
cs.CV eess.IV
http://creativecommons.org/publicdomain/zero/1.0/
Normal reconstruction is crucial in non-line-of-sight (NLOS) imaging, as it provides key geometric and lighting information about hidden objects, which significantly improves reconstruction accuracy and scene understanding. However, jointly estimating normals and albedo expands the problem from matrix-valued functions to tensor-valued functions that substantially increasing complexity and computational difficulty. In this paper, we propose a novel joint albedo-surface reconstruction method, which utilizes the Frobenius norm of the shape operator to control the variation rate of the normal field. It is the first attempt to apply regularization methods to the reconstruction of surface normals for hidden objects. By improving the accuracy of the normal field, it enhances detail representation and achieves high-precision reconstruction of hidden object geometry. The proposed method demonstrates robustness and effectiveness on both synthetic and experimental datasets. On transient data captured within 15 seconds, our surface normal-regularized reconstruction model produces more accurate surfaces than recently proposed methods and is 30 times faster than the existing surface reconstruction approach.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 08:56:00 GMT" } ]
2025-03-25T00:00:00
[ [ "Liu", "Xueying", "" ], [ "Wang", "Lianfang", "" ], [ "Liu", "Jun", "" ], [ "Wang", "Yong", "" ], [ "Duan", "Yuping", "" ] ]
TITLE: Geometric Constrained Non-Line-of-Sight Imaging ABSTRACT: Normal reconstruction is crucial in non-line-of-sight (NLOS) imaging, as it provides key geometric and lighting information about hidden objects, which significantly improves reconstruction accuracy and scene understanding. However, jointly estimating normals and albedo expands the problem from matrix-valued functions to tensor-valued functions that substantially increasing complexity and computational difficulty. In this paper, we propose a novel joint albedo-surface reconstruction method, which utilizes the Frobenius norm of the shape operator to control the variation rate of the normal field. It is the first attempt to apply regularization methods to the reconstruction of surface normals for hidden objects. By improving the accuracy of the normal field, it enhances detail representation and achieves high-precision reconstruction of hidden object geometry. The proposed method demonstrates robustness and effectiveness on both synthetic and experimental datasets. On transient data captured within 15 seconds, our surface normal-regularized reconstruction model produces more accurate surfaces than recently proposed methods and is 30 times faster than the existing surface reconstruction approach.
2503.17993
Patrick Ebel
Jussi Jokinen, Patrick Ebel, Tuomo Kujala
Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control
null
null
null
null
cs.HC cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 08:56:53 GMT" } ]
2025-03-25T00:00:00
[ [ "Jokinen", "Jussi", "" ], [ "Ebel", "Patrick", "" ], [ "Kujala", "Tuomo", "" ] ]
TITLE: Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control ABSTRACT: Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.
2503.17998
Junaed Younus Khan
Navid Bin Hasan, Md. Ashraful Islam, Junaed Younus Khan, Sanjida Senjik, Anindya Iqbal
Automatic High-Level Test Case Generation using Large Language Models
Accepted at International Conference on Mining Software Repositories (MSR) 2025
null
null
null
cs.SE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructed a use-case $\rightarrow$ (high-level) test-cases dataset to train/fine-tune models for generating high-level test cases. High-level test cases specify what aspects of the software's functionality need to be tested, along with the expected outcomes. We evaluated large language models, such as GPT-4o, Gemini, LLaMA 3.1 8B, and Mistral 7B, where fine-tuning (the latter two) yields improved performance. A final (human evaluation) survey confirmed the effectiveness of these generated test cases. Our proactive approach strengthens requirement-testing alignment and facilitates early test case generation to streamline development.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 09:14:41 GMT" } ]
2025-03-25T00:00:00
[ [ "Hasan", "Navid Bin", "" ], [ "Islam", "Md. Ashraful", "" ], [ "Khan", "Junaed Younus", "" ], [ "Senjik", "Sanjida", "" ], [ "Iqbal", "Anindya", "" ] ]
TITLE: Automatic High-Level Test Case Generation using Large Language Models ABSTRACT: We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructed a use-case $\rightarrow$ (high-level) test-cases dataset to train/fine-tune models for generating high-level test cases. High-level test cases specify what aspects of the software's functionality need to be tested, along with the expected outcomes. We evaluated large language models, such as GPT-4o, Gemini, LLaMA 3.1 8B, and Mistral 7B, where fine-tuning (the latter two) yields improved performance. A final (human evaluation) survey confirmed the effectiveness of these generated test cases. Our proactive approach strengthens requirement-testing alignment and facilitates early test case generation to streamline development.
2503.18001
Kunal Mukherjee
Kunal Mukherjee, Zachary Harrison, Saeid Balaneshin
Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations
null
null
null
null
cs.IR cs.LG cs.SI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Transparency and interpretability are crucial for enhancing customer confidence and user engagement, especially when dealing with black-box Machine Learning (ML)-based recommendation systems. Modern recommendation systems leverage Graph Neural Network (GNN) due to their ability to produce high-quality recommendations in terms of both relevance and diversity. Therefore, the explainability of GNN is especially important for Link Prediction (LP) tasks since recommending relevant items can be viewed as predicting links between users and items. GNN explainability has been a well-studied field, existing methods primarily focus on node or graph-level tasks, leaving a gap in LP explanation techniques. This work introduces Z-REx, a GNN explanation framework designed explicitly for heterogeneous link prediction tasks. Z-REx utilizes structural and attribute perturbation to identify critical sub-structures and important features while reducing the search space by leveraging domain-specific knowledge. In our experimentation, we show the efficacy of Z-REx in generating contextually relevant and human-interpretable explanations for ZiGNN, a GNN-based recommendation engine, using a real-world real-estate dataset from Zillow Group, Inc. We also compare Z-REx to State-of-The-Art (SOTA) GNN explainers to show Z-REx's superiority in producing high-quality human-interpretable explanations.
[ { "version": "v1", "created": "Wed, 12 Feb 2025 02:42:25 GMT" } ]
2025-03-25T00:00:00
[ [ "Mukherjee", "Kunal", "" ], [ "Harrison", "Zachary", "" ], [ "Balaneshin", "Saeid", "" ] ]
TITLE: Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations ABSTRACT: Transparency and interpretability are crucial for enhancing customer confidence and user engagement, especially when dealing with black-box Machine Learning (ML)-based recommendation systems. Modern recommendation systems leverage Graph Neural Network (GNN) due to their ability to produce high-quality recommendations in terms of both relevance and diversity. Therefore, the explainability of GNN is especially important for Link Prediction (LP) tasks since recommending relevant items can be viewed as predicting links between users and items. GNN explainability has been a well-studied field, existing methods primarily focus on node or graph-level tasks, leaving a gap in LP explanation techniques. This work introduces Z-REx, a GNN explanation framework designed explicitly for heterogeneous link prediction tasks. Z-REx utilizes structural and attribute perturbation to identify critical sub-structures and important features while reducing the search space by leveraging domain-specific knowledge. In our experimentation, we show the efficacy of Z-REx in generating contextually relevant and human-interpretable explanations for ZiGNN, a GNN-based recommendation engine, using a real-world real-estate dataset from Zillow Group, Inc. We also compare Z-REx to State-of-The-Art (SOTA) GNN explainers to show Z-REx's superiority in producing high-quality human-interpretable explanations.
2503.18007
Hongyu Yan
Hongyu Yan, Zijun Li, Kunming Luo, Li Lu, Ping Tan
SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance
Accepted by AAAI 2025 (Oral presentation), Code: https://github.com/HongyuYann/SymmCompletion
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cs.CV
http://creativecommons.org/licenses/by/4.0/
Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we introduce SymmCompletion, a highly effective completion method based on symmetry guidance. Our method comprises two primary components: a Local Symmetry Transformation Network (LSTNet) and a Symmetry-Guidance Transformer (SGFormer). First, LSTNet efficiently estimates point-wise local symmetry transformation to transform key geometries of partial inputs into missing regions, thereby generating geometry-align partial-missing pairs and initial point clouds. Second, SGFormer leverages the geometric features of partial-missing pairs as the explicit symmetric guidance that can constrain the refinement process for initial point clouds. As a result, SGFormer can exploit provided priors to form high-fidelity and geometry-consistency final point clouds. Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks.
[ { "version": "v1", "created": "Sun, 23 Mar 2025 09:45:37 GMT" } ]
2025-03-25T00:00:00
[ [ "Yan", "Hongyu", "" ], [ "Li", "Zijun", "" ], [ "Luo", "Kunming", "" ], [ "Lu", "Li", "" ], [ "Tan", "Ping", "" ] ]
TITLE: SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance ABSTRACT: Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we introduce SymmCompletion, a highly effective completion method based on symmetry guidance. Our method comprises two primary components: a Local Symmetry Transformation Network (LSTNet) and a Symmetry-Guidance Transformer (SGFormer). First, LSTNet efficiently estimates point-wise local symmetry transformation to transform key geometries of partial inputs into missing regions, thereby generating geometry-align partial-missing pairs and initial point clouds. Second, SGFormer leverages the geometric features of partial-missing pairs as the explicit symmetric guidance that can constrain the refinement process for initial point clouds. As a result, SGFormer can exploit provided priors to form high-fidelity and geometry-consistency final point clouds. Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks.