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value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2308.15673
|
Bhaskar Ramasubramanian
|
Arezoo Rajabi, Surudhi Asokraj, Fengqing Jiang, Luyao Niu, Bhaskar
Ramasubramanian, Jim Ritcey, Radha Poovendran
|
MDTD: A Multi Domain Trojan Detector for Deep Neural Networks
|
Accepted to ACM Conference on Computer and Communications Security
(ACM CCS) 2023
| null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Machine learning models that use deep neural networks (DNNs) are vulnerable
to backdoor attacks. An adversary carrying out a backdoor attack embeds a
predefined perturbation called a trigger into a small subset of input samples
and trains the DNN such that the presence of the trigger in the input results
in an adversary-desired output class. Such adversarial retraining however needs
to ensure that outputs for inputs without the trigger remain unaffected and
provide high classification accuracy on clean samples. In this paper, we
propose MDTD, a Multi-Domain Trojan Detector for DNNs, which detects inputs
containing a Trojan trigger at testing time. MDTD does not require knowledge of
trigger-embedding strategy of the attacker and can be applied to a pre-trained
DNN model with image, audio, or graph-based inputs. MDTD leverages an insight
that input samples containing a Trojan trigger are located relatively farther
away from a decision boundary than clean samples. MDTD estimates the distance
to a decision boundary using adversarial learning methods and uses this
distance to infer whether a test-time input sample is Trojaned or not. We
evaluate MDTD against state-of-the-art Trojan detection methods across five
widely used image-based datasets: CIFAR100, CIFAR10, GTSRB, SVHN, and
Flowers102; four graph-based datasets: AIDS, WinMal, Toxicant, and COLLAB; and
the SpeechCommand audio dataset. MDTD effectively identifies samples that
contain different types of Trojan triggers. We evaluate MDTD against adaptive
attacks where an adversary trains a robust DNN to increase (decrease) distance
of benign (Trojan) inputs from a decision boundary.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 00:03:03 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Sep 2023 01:59:49 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Rajabi",
"Arezoo",
""
],
[
"Asokraj",
"Surudhi",
""
],
[
"Jiang",
"Fengqing",
""
],
[
"Niu",
"Luyao",
""
],
[
"Ramasubramanian",
"Bhaskar",
""
],
[
"Ritcey",
"Jim",
""
],
[
"Poovendran",
"Radha",
""
]
] |
new_dataset
| 0.974977 |
2308.16481
|
Ahmed Hatem
|
Ahmed Hatem, Yiming Qian, Yang Wang
|
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using
Multitask Meta-Auxiliary Learning
|
Accepted at ICCV 2023
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present Point-TTA, a novel test-time adaptation framework for point cloud
registration (PCR) that improves the generalization and the performance of
registration models. While learning-based approaches have achieved impressive
progress, generalization to unknown testing environments remains a major
challenge due to the variations in 3D scans. Existing methods typically train a
generic model and the same trained model is applied on each instance during
testing. This could be sub-optimal since it is difficult for the same model to
handle all the variations during testing. In this paper, we propose a test-time
adaptation approach for PCR. Our model can adapt to unseen distributions at
test-time without requiring any prior knowledge of the test data. Concretely,
we design three self-supervised auxiliary tasks that are optimized jointly with
the primary PCR task. Given a test instance, we adapt our model using these
auxiliary tasks and the updated model is used to perform the inference. During
training, our model is trained using a meta-auxiliary learning approach, such
that the adapted model via auxiliary tasks improves the accuracy of the primary
task. Experimental results demonstrate the effectiveness of our approach in
improving generalization of point cloud registration and outperforming other
state-of-the-art approaches.
|
[
{
"version": "v1",
"created": "Thu, 31 Aug 2023 06:32:11 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 18:13:58 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Hatem",
"Ahmed",
""
],
[
"Qian",
"Yiming",
""
],
[
"Wang",
"Yang",
""
]
] |
new_dataset
| 0.976061 |
2308.16890
|
Shuai Bai
|
Shuai Bai, Shusheng Yang, Jinze Bai, Peng Wang, Xingxuan Zhang,
Junyang Lin, Xinggang Wang, Chang Zhou, Jingren Zhou
|
TouchStone: Evaluating Vision-Language Models by Language Models
|
https://github.com/OFA-Sys/TouchStone
| null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large vision-language models (LVLMs) have recently witnessed rapid
advancements, exhibiting a remarkable capacity for perceiving, understanding,
and processing visual information by connecting visual receptor with large
language models (LLMs). However, current assessments mainly focus on
recognizing and reasoning abilities, lacking direct evaluation of
conversational skills and neglecting visual storytelling abilities. In this
paper, we propose an evaluation method that uses strong LLMs as judges to
comprehensively evaluate the various abilities of LVLMs. Firstly, we construct
a comprehensive visual dialogue dataset TouchStone, consisting of open-world
images and questions, covering five major categories of abilities and 27
subtasks. This dataset not only covers fundamental recognition and
comprehension but also extends to literary creation. Secondly, by integrating
detailed image annotations we effectively transform the multimodal input
content into a form understandable by LLMs. This enables us to employ advanced
LLMs for directly evaluating the quality of the multimodal dialogue without
requiring human intervention. Through validation, we demonstrate that powerful
LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging
their textual capabilities alone, aligning with human preferences. We hope our
work can serve as a touchstone for LVLMs' evaluation and pave the way for
building stronger LVLMs. The evaluation code is available at
https://github.com/OFA-Sys/TouchStone.
|
[
{
"version": "v1",
"created": "Thu, 31 Aug 2023 17:52:04 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Sep 2023 15:06:15 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Bai",
"Shuai",
""
],
[
"Yang",
"Shusheng",
""
],
[
"Bai",
"Jinze",
""
],
[
"Wang",
"Peng",
""
],
[
"Zhang",
"Xingxuan",
""
],
[
"Lin",
"Junyang",
""
],
[
"Wang",
"Xinggang",
""
],
[
"Zhou",
"Chang",
""
],
[
"Zhou",
"Jingren",
""
]
] |
new_dataset
| 0.981101 |
2309.00428
|
Xiaoyu Pan
|
Xiaoyu Pan, Bowen Zheng, Xinwei Jiang, Guanglong Xu, Xianli Gu,
Jingxiang Li, Qilong Kou, He Wang, Tianjia Shao, Kun Zhou and Xiaogang Jin
|
A Locality-based Neural Solver for Optical Motion Capture
|
Siggraph Asia 2023 Conference Paper
| null |
10.1145/3610548.3618148
| null |
cs.GR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel locality-based learning method for cleaning and solving
optical motion capture data. Given noisy marker data, we propose a new
heterogeneous graph neural network which treats markers and joints as different
types of nodes, and uses graph convolution operations to extract the local
features of markers and joints and transform them to clean motions. To deal
with anomaly markers (e.g. occluded or with big tracking errors), the key
insight is that a marker's motion shows strong correlations with the motions of
its immediate neighboring markers but less so with other markers, a.k.a.
locality, which enables us to efficiently fill missing markers (e.g. due to
occlusion). Additionally, we also identify marker outliers due to tracking
errors by investigating their acceleration profiles. Finally, we propose a
training regime based on representation learning and data augmentation, by
training the model on data with masking. The masking schemes aim to mimic the
occluded and noisy markers often observed in the real data. Finally, we show
that our method achieves high accuracy on multiple metrics across various
datasets. Extensive comparison shows our method outperforms state-of-the-art
methods in terms of prediction accuracy of occluded marker position error by
approximately 20%, which leads to a further error reduction on the
reconstructed joint rotations and positions by 30%. The code and data for this
paper are available at https://github.com/non-void/LocalMoCap.
|
[
{
"version": "v1",
"created": "Fri, 1 Sep 2023 12:40:17 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Sep 2023 09:21:14 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Pan",
"Xiaoyu",
""
],
[
"Zheng",
"Bowen",
""
],
[
"Jiang",
"Xinwei",
""
],
[
"Xu",
"Guanglong",
""
],
[
"Gu",
"Xianli",
""
],
[
"Li",
"Jingxiang",
""
],
[
"Kou",
"Qilong",
""
],
[
"Wang",
"He",
""
],
[
"Shao",
"Tianjia",
""
],
[
"Zhou",
"Kun",
""
],
[
"Jin",
"Xiaogang",
""
]
] |
new_dataset
| 0.995057 |
2309.00682
|
Burak Bartan
|
Burak Bartan and Mert Pilanci
|
Randomized Polar Codes for Anytime Distributed Machine Learning
| null | null | null | null |
cs.DC cs.IT cs.LG math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel distributed computing framework that is robust to slow
compute nodes, and is capable of both approximate and exact computation of
linear operations. The proposed mechanism integrates the concepts of randomized
sketching and polar codes in the context of coded computation. We propose a
sequential decoding algorithm designed to handle real valued data while
maintaining low computational complexity for recovery. Additionally, we provide
an anytime estimator that can generate provably accurate estimates even when
the set of available node outputs is not decodable. We demonstrate the
potential applications of this framework in various contexts, such as
large-scale matrix multiplication and black-box optimization. We present the
implementation of these methods on a serverless cloud computing system and
provide numerical results to demonstrate their scalability in practice,
including ImageNet scale computations.
|
[
{
"version": "v1",
"created": "Fri, 1 Sep 2023 18:02:04 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Bartan",
"Burak",
""
],
[
"Pilanci",
"Mert",
""
]
] |
new_dataset
| 0.995156 |
2309.00687
|
Gabor P. Nagy
|
M\'arton Erd\'elyi and P\'al Heged\"us and S\'andor Z. Kiss and
G\'abor P. Nagy
|
On Linear Codes with Random Multiplier Vectors and the Maximum Trace
Dimension Property
| null | null | null | null |
cs.IT math.IT math.NT
|
http://creativecommons.org/licenses/by/4.0/
|
Let $C$ be a linear code of length $n$ and dimension $k$ over the finite
field $\mathbb{F}_{q^m}$. The trace code $\mathrm{Tr}(C)$ is a linear code of
the same length $n$ over the subfield $\mathbb{F}_q$. The obvious upper bound
for the dimension of the trace code over $\mathbb{F}_q$ is $mk$. If equality
holds, then we say that $C$ has maximum trace dimension. The problem of finding
the true dimension of trace codes and their duals is relevant for the size of
the public key of various code-based cryptographic protocols. Let
$C_{\mathbf{a}}$ denote the code obtained from $C$ and a multiplier vector
$\mathbf{a}\in (\mathbb{F}_{q^m})^n$. In this paper, we give a lower bound for
the probability that a random multiplier vector produces a code
$C_{\mathbf{a}}$ of maximum trace dimension. We give an interpretation of the
bound for the class of algebraic geometry codes in terms of the degree of the
defining divisor. The bound explains the experimental fact that random
alternant codes have minimal dimension. Our bound holds whenever $n\geq
m(k+h)$, where $h\geq 0$ is the Singleton defect of $C$. For the extremal case
$n=m(h+k)$, numerical experiments reveal a closed connection between the
probability of having maximum trace dimension and the probability that a random
matrix has full rank.
|
[
{
"version": "v1",
"created": "Fri, 1 Sep 2023 18:13:23 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Erdélyi",
"Márton",
""
],
[
"Hegedüs",
"Pál",
""
],
[
"Kiss",
"Sándor Z.",
""
],
[
"Nagy",
"Gábor P.",
""
]
] |
new_dataset
| 0.990332 |
2309.00743
|
Divyanshu Raj
|
Divyanshu Raj, Chitta Baral, Nakul Gopalan
|
Language-Conditioned Change-point Detection to Identify Sub-Tasks in
Robotics Domains
|
9 Pages, 13 figures, Accepted paper at the RSS 2023 Workshop on
Articulate Robots: Utilizing Language for Robot Learning
| null | null | null |
cs.RO cs.AI cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this work, we present an approach to identify sub-tasks within a
demonstrated robot trajectory using language instructions. We identify these
sub-tasks using language provided during demonstrations as guidance to identify
sub-segments of a longer robot trajectory. Given a sequence of natural language
instructions and a long trajectory consisting of image frames and discrete
actions, we want to map an instruction to a smaller fragment of the trajectory.
Unlike previous instruction following works which directly learn the mapping
from language to a policy, we propose a language-conditioned change-point
detection method to identify sub-tasks in a problem. Our approach learns the
relationship between constituent segments of a long language command and
corresponding constituent segments of a trajectory. These constituent
trajectory segments can be used to learn subtasks or sub-goals for planning or
options as demonstrated by previous related work. Our insight in this work is
that the language-conditioned robot change-point detection problem is similar
to the existing video moment retrieval works used to identify sub-segments
within online videos. Through extensive experimentation, we demonstrate a
$1.78_{\pm 0.82}\%$ improvement over a baseline approach in accurately
identifying sub-tasks within a trajectory using our proposed method. Moreover,
we present a comprehensive study investigating sample complexity requirements
on learning this mapping, between language and trajectory sub-segments, to
understand if the video retrieval-based methods are realistic in real robot
scenarios.
|
[
{
"version": "v1",
"created": "Fri, 1 Sep 2023 21:40:34 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Raj",
"Divyanshu",
""
],
[
"Baral",
"Chitta",
""
],
[
"Gopalan",
"Nakul",
""
]
] |
new_dataset
| 0.998881 |
2309.00789
|
Melissa Dell
|
Abhishek Arora, Melissa Dell
|
LinkTransformer: A Unified Package for Record Linkage with Transformer
Language Models
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Linking information across sources is fundamental to a variety of analyses in
social science, business, and government. While large language models (LLMs)
offer enormous promise for improving record linkage in noisy datasets, in many
domains approximate string matching packages in popular softwares such as R and
Stata remain predominant. These packages have clean, simple interfaces and can
be easily extended to a diversity of languages. Our open-source package
LinkTransformer aims to extend the familiarity and ease-of-use of popular
string matching methods to deep learning. It is a general purpose package for
record linkage with transformer LLMs that treats record linkage as a text
retrieval problem. At its core is an off-the-shelf toolkit for applying
transformer models to record linkage with four lines of code. LinkTransformer
contains a rich repository of pre-trained transformer semantic similarity
models for multiple languages and supports easy integration of any transformer
language model from Hugging Face or OpenAI. It supports standard functionality
such as blocking and linking on multiple noisy fields. LinkTransformer APIs
also perform other common text data processing tasks, e.g., aggregation, noisy
de-duplication, and translation-free cross-lingual linkage. Importantly,
LinkTransformer also contains comprehensive tools for efficient model tuning,
to facilitate different levels of customization when off-the-shelf models do
not provide the required accuracy. Finally, to promote reusability,
reproducibility, and extensibility, LinkTransformer makes it easy for users to
contribute their custom-trained models to its model hub. By combining
transformer language models with intuitive APIs that will be familiar to many
users of popular string matching packages, LinkTransformer aims to democratize
the benefits of LLMs among those who may be less familiar with deep learning
frameworks.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 01:45:27 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Arora",
"Abhishek",
""
],
[
"Dell",
"Melissa",
""
]
] |
new_dataset
| 0.969753 |
2309.00790
|
Sikai Chen
|
Runjia Du, Pei Li, Sikai Chen, Samuel Labi
|
PFL-LSTR: A privacy-preserving framework for driver intention inference
based on in-vehicle and out-vehicle information
|
Submitted for presentation only at the 2024 Annual Meeting of the
Transportation Research Board
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Intelligent vehicle anticipation of the movement intentions of other drivers
can reduce collisions. Typically, when a human driver of another vehicle
(referred to as the target vehicle) engages in specific behaviors such as
checking the rearview mirror prior to lane change, a valuable clue is therein
provided on the intentions of the target vehicle's driver. Furthermore, the
target driver's intentions can be influenced and shaped by their driving
environment. For example, if the target vehicle is too close to a leading
vehicle, it may renege the lane change decision. On the other hand, a following
vehicle in the target lane is too close to the target vehicle could lead to its
reversal of the decision to change lanes. Knowledge of such intentions of all
vehicles in a traffic stream can help enhance traffic safety. Unfortunately,
such information is often captured in the form of images/videos. Utilization of
personally identifiable data to train a general model could violate user
privacy. Federated Learning (FL) is a promising tool to resolve this conundrum.
FL efficiently trains models without exposing the underlying data. This paper
introduces a Personalized Federated Learning (PFL) model embedded a long
short-term transformer (LSTR) framework. The framework predicts drivers'
intentions by leveraging in-vehicle videos (of driver movement, gestures, and
expressions) and out-of-vehicle videos (of the vehicle's surroundings -
frontal/rear areas). The proposed PFL-LSTR framework is trained and tested
through real-world driving data collected from human drivers at Interstate 65
in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability
and high precision, and that out-of-vehicle information (particularly, the
driver's rear-mirror viewing actions) is important because it helps reduce
false positives and thereby enhances the precision of driver intention
inference.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 01:51:41 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Du",
"Runjia",
""
],
[
"Li",
"Pei",
""
],
[
"Chen",
"Sikai",
""
],
[
"Labi",
"Samuel",
""
]
] |
new_dataset
| 0.999187 |
2309.00794
|
Shibei Meng
|
Shibei Meng, Yang Fu, Saihui Hou, Chunshui Cao, Xu Liu, Yongzhen Huang
|
FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait
Recognition
|
10 pages, 4 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present FastPoseGait, an open-source toolbox for pose-based gait
recognition based on PyTorch. Our toolbox supports a set of cutting-edge
pose-based gait recognition algorithms and a variety of related benchmarks.
Unlike other pose-based projects that focus on a single algorithm, FastPoseGait
integrates several state-of-the-art (SOTA) algorithms under a unified
framework, incorporating both the latest advancements and best practices to
ease the comparison of effectiveness and efficiency. In addition, to promote
future research on pose-based gait recognition, we provide numerous pre-trained
models and detailed benchmark results, which offer valuable insights and serve
as a reference for further investigations. By leveraging the highly modular
structure and diverse methods offered by FastPoseGait, researchers can quickly
delve into pose-based gait recognition and promote development in the field. In
this paper, we outline various features of this toolbox, aiming that our
toolbox and benchmarks can further foster collaboration, facilitate
reproducibility, and encourage the development of innovative algorithms for
pose-based gait recognition. FastPoseGait is available at
https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will
continue updating this report as we add new features.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 02:05:58 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Meng",
"Shibei",
""
],
[
"Fu",
"Yang",
""
],
[
"Hou",
"Saihui",
""
],
[
"Cao",
"Chunshui",
""
],
[
"Liu",
"Xu",
""
],
[
"Huang",
"Yongzhen",
""
]
] |
new_dataset
| 0.993604 |
2309.00796
|
Chongyang Zhong
|
Chongyang Zhong, Lei Hu, Zihao Zhang, Shihong Xia
|
AttT2M: Text-Driven Human Motion Generation with Multi-Perspective
Attention Mechanism
|
IEEE International Conference on Computer Vision 2023, 9 pages
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Generating 3D human motion based on textual descriptions has been a research
focus in recent years. It requires the generated motion to be diverse, natural,
and conform to the textual description. Due to the complex spatio-temporal
nature of human motion and the difficulty in learning the cross-modal
relationship between text and motion, text-driven motion generation is still a
challenging problem. To address these issues, we propose \textbf{AttT2M}, a
two-stage method with multi-perspective attention mechanism: \textbf{body-part
attention} and \textbf{global-local motion-text attention}. The former focuses
on the motion embedding perspective, which means introducing a body-part
spatio-temporal encoder into VQ-VAE to learn a more expressive discrete latent
space. The latter is from the cross-modal perspective, which is used to learn
the sentence-level and word-level motion-text cross-modal relationship. The
text-driven motion is finally generated with a generative transformer.
Extensive experiments conducted on HumanML3D and KIT-ML demonstrate that our
method outperforms the current state-of-the-art works in terms of qualitative
and quantitative evaluation, and achieve fine-grained synthesis and
action2motion. Our code is in https://github.com/ZcyMonkey/AttT2M
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 02:18:17 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Zhong",
"Chongyang",
""
],
[
"Hu",
"Lei",
""
],
[
"Zhang",
"Zihao",
""
],
[
"Xia",
"Shihong",
""
]
] |
new_dataset
| 0.996746 |
2309.00817
|
Yida Chen
|
Yida Chen, Kang Liu, Yi Xin, Xinru Zhao
|
Soil Image Segmentation Based on Mask R-CNN
|
4 pages, 5 figures, Published in 2023 3rd International Conference on
Consumer Electronics and Computer Engineering
|
2023 3rd International Conference on Consumer Electronics and
Computer Engineering (ICCECE)
|
10.1109/ICCECE58074.2023.10135317
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The complex background in the soil image collected in the field natural
environment will affect the subsequent soil image recognition based on machine
vision. Segmenting the soil center area from the soil image can eliminate the
influence of the complex background, which is an important preprocessing work
for subsequent soil image recognition. For the first time, the deep learning
method was applied to soil image segmentation, and the Mask R-CNN model was
selected to complete the positioning and segmentation of soil images. Construct
a soil image dataset based on the collected soil images, use the EISeg
annotation tool to mark the soil area as soil, and save the annotation
information; train the Mask R-CNN soil image instance segmentation model. The
trained model can obtain accurate segmentation results for soil images, and can
show good performance on soil images collected in different environments; the
trained instance segmentation model has a loss value of 0.1999 in the training
set, and the mAP of the validation set segmentation (IoU=0.5) is 0.8804, and it
takes only 0.06s to complete image segmentation based on GPU acceleration,
which can meet the real-time segmentation and detection of soil images in the
field under natural conditions. You can get our code in the Conclusions. The
homepage is https://github.com/YidaMyth.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 04:08:06 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Chen",
"Yida",
""
],
[
"Liu",
"Kang",
""
],
[
"Xin",
"Yi",
""
],
[
"Zhao",
"Xinru",
""
]
] |
new_dataset
| 0.990593 |
2309.00842
|
Rishi Vanukuru
|
Rishi Vanukuru, Suibi Che-Chuan Weng, Krithik Ranjan, Torin Hopkins,
Amy Banic, Mark D. Gross, Ellen Yi-Luen Do
|
DualStream: Spatially Sharing Selves and Surroundings using Mobile
Devices and Augmented Reality
|
10 pages, 4 figures, 1 table; To appear in the proceedings of the
IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2023
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In-person human interaction relies on our spatial perception of each other
and our surroundings. Current remote communication tools partially address each
of these aspects. Video calls convey real user representations but without
spatial interactions. Augmented and Virtual Reality (AR/VR) experiences are
immersive and spatial but often use virtual environments and characters instead
of real-life representations. Bridging these gaps, we introduce DualStream, a
system for synchronous mobile AR remote communication that captures, streams,
and displays spatial representations of users and their surroundings.
DualStream supports transitions between user and environment representations
with different levels of visuospatial fidelity, as well as the creation of
persistent shared spaces using environment snapshots. We demonstrate how
DualStream can enable spatial communication in real-world contexts, and support
the creation of blended spaces for collaboration. A formative evaluation of
DualStream revealed that users valued the ability to interact spatially and
move between representations, and could see DualStream fitting into their own
remote communication practices in the near future. Drawing from these findings,
we discuss new opportunities for designing more widely accessible spatial
communication tools, centered around the mobile phone.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 06:38:33 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Vanukuru",
"Rishi",
""
],
[
"Weng",
"Suibi Che-Chuan",
""
],
[
"Ranjan",
"Krithik",
""
],
[
"Hopkins",
"Torin",
""
],
[
"Banic",
"Amy",
""
],
[
"Gross",
"Mark D.",
""
],
[
"Do",
"Ellen Yi-Luen",
""
]
] |
new_dataset
| 0.997119 |
2309.00898
|
Maksym Planeta
|
Maksym Planeta, Jan Bierbaum, Michael Roitzsch, Hermann H\"artig
|
CoRD: Converged RDMA Dataplane for High-Performance Clouds
|
11 pages
| null | null | null |
cs.OS
|
http://creativecommons.org/licenses/by/4.0/
|
High-performance networking is often characterized by kernel bypass which is
considered mandatory in high-performance parallel and distributed applications.
But kernel bypass comes at a price because it breaks the traditional OS
architecture, requiring applications to use special APIs and limiting the OS
control over existing network connections. We make the case, that kernel bypass
is not mandatory. Rather, high-performance networking relies on multiple
performance-improving techniques, with kernel bypass being the least effective.
CoRD removes kernel bypass from RDMA networks, enabling efficient OS-level
control over RDMA dataplane.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 10:25:34 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Planeta",
"Maksym",
""
],
[
"Bierbaum",
"Jan",
""
],
[
"Roitzsch",
"Michael",
""
],
[
"Härtig",
"Hermann",
""
]
] |
new_dataset
| 0.989668 |
2309.00916
|
Chen Wang
|
Chen Wang, Minpeng Liao, Zhongqiang Huang, Jinliang Lu, Junhong Wu,
Yuchen Liu, Chengqing Zong, Jiajun Zhang
|
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment
of Continuation Writing
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
The emergence of large language models (LLMs) has sparked significant
interest in extending their remarkable language capabilities to speech.
However, modality alignment between speech and text still remains an open
problem. Current solutions can be categorized into two strategies. One is a
cascaded approach where outputs (tokens or states) of a separately trained
speech recognition system are used as inputs for LLMs, which limits their
potential in modeling alignment between speech and text. The other is an
end-to-end approach that relies on speech instruction data, which is very
difficult to collect in large quantities. In this paper, we address these
issues and propose the BLSP approach that Bootstraps Language-Speech
Pre-training via behavior alignment of continuation writing. We achieve this by
learning a lightweight modality adapter between a frozen speech encoder and an
LLM, ensuring that the LLM exhibits the same generation behavior regardless of
the modality of input: a speech segment or its transcript. The training process
can be divided into two steps. The first step prompts an LLM to generate texts
with speech transcripts as prefixes, obtaining text continuations. In the
second step, these continuations are used as supervised signals to train the
modality adapter in an end-to-end manner. We demonstrate that this
straightforward process can extend the capabilities of LLMs to speech, enabling
speech recognition, speech translation, spoken language understanding, and
speech conversation, even in zero-shot cross-lingual scenarios.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 11:46:05 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Wang",
"Chen",
""
],
[
"Liao",
"Minpeng",
""
],
[
"Huang",
"Zhongqiang",
""
],
[
"Lu",
"Jinliang",
""
],
[
"Wu",
"Junhong",
""
],
[
"Liu",
"Yuchen",
""
],
[
"Zong",
"Chengqing",
""
],
[
"Zhang",
"Jiajun",
""
]
] |
new_dataset
| 0.994249 |
2309.00928
|
Kailun Yang
|
Xuan He, Kailun Yang, Junwei Zheng, Jin Yuan, Luis M. Bergasa, Hui
Zhang, Zhiyong Li
|
S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer
for Monocular 3D Object Detection
|
The source code will be made publicly available at
https://github.com/mikasa3lili/S3-MonoDETR
| null | null | null |
cs.CV cs.RO eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, transformer-based methods have shown exceptional performance in
monocular 3D object detection, which can predict 3D attributes from a single 2D
image. These methods typically use visual and depth representations to generate
query points on objects, whose quality plays a decisive role in the detection
accuracy. However, current unsupervised attention mechanisms without any
geometry appearance awareness in transformers are susceptible to producing
noisy features for query points, which severely limits the network performance
and also makes the model have a poor ability to detect multi-category objects
in a single training process. To tackle this problem, this paper proposes a
novel "Supervised Shape&Scale-perceptive Deformable Attention" (S$^3$-DA)
module for monocular 3D object detection. Concretely, S$^3$-DA utilizes visual
and depth features to generate diverse local features with various shapes and
scales and predict the corresponding matching distribution simultaneously to
impose valuable shape&scale perception for each query. Benefiting from this,
S$^3$-DA effectively estimates receptive fields for query points belonging to
any category, enabling them to generate robust query features. Besides, we
propose a Multi-classification-based Shape$\&$Scale Matching (MSM) loss to
supervise the above process. Extensive experiments on KITTI and Waymo Open
datasets demonstrate that S$^3$-DA significantly improves the detection
accuracy, yielding state-of-the-art performance of single-category and
multi-category 3D object detection in a single training process compared to the
existing approaches. The source code will be made publicly available at
https://github.com/mikasa3lili/S3-MonoDETR.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 12:36:38 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"He",
"Xuan",
""
],
[
"Yang",
"Kailun",
""
],
[
"Zheng",
"Junwei",
""
],
[
"Yuan",
"Jin",
""
],
[
"Bergasa",
"Luis M.",
""
],
[
"Zhang",
"Hui",
""
],
[
"Li",
"Zhiyong",
""
]
] |
new_dataset
| 0.967536 |
2309.00929
|
Qing Wang
|
Qing Wang, Jixun Yao, Li Zhang, Pengcheng Guo, and Lei Xie
|
Timbre-reserved Adversarial Attack in Speaker Identification
|
11 pages, 8 figures
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As a type of biometric identification, a speaker identification (SID) system
is confronted with various kinds of attacks. The spoofing attacks typically
imitate the timbre of the target speakers, while the adversarial attacks
confuse the SID system by adding a well-designed adversarial perturbation to an
arbitrary speech. Although the spoofing attack copies a similar timbre as the
victim, it does not exploit the vulnerability of the SID model and may not make
the SID system give the attacker's desired decision. As for the adversarial
attack, despite the SID system can be led to a designated decision, it cannot
meet the specified text or speaker timbre requirements for the specific attack
scenarios. In this study, to make the attack in SID not only leverage the
vulnerability of the SID model but also reserve the timbre of the target
speaker, we propose a timbre-reserved adversarial attack in the speaker
identification. We generate the timbre-reserved adversarial audios by adding an
adversarial constraint during the different training stages of the voice
conversion (VC) model. Specifically, the adversarial constraint is using the
target speaker label to optimize the adversarial perturbation added to the VC
model representations and is implemented by a speaker classifier joining in the
VC model training. The adversarial constraint can help to control the VC model
to generate the speaker-wised audio. Eventually, the inference of the VC model
is the ideal adversarial fake audio, which is timbre-reserved and can fool the
SID system.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 12:42:03 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Wang",
"Qing",
""
],
[
"Yao",
"Jixun",
""
],
[
"Zhang",
"Li",
""
],
[
"Guo",
"Pengcheng",
""
],
[
"Xie",
"Lei",
""
]
] |
new_dataset
| 0.997683 |
2309.00944
|
Soumya Parekh
|
Soumya Parekh, Jay Patel
|
Pressmatch: Automated journalist recommendation for media coverage with
Nearest Neighbor search
|
11 pages, 8 figures
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Slating a product for release often involves pitching journalists to run
stories on your press release. Good media coverage often ensures greater
product reach and drives audience engagement for those products. Hence,
ensuring that those releases are pitched to the right journalists with relevant
interests is crucial, since they receive several pitches daily. Keeping up with
journalist beats and curating a media contacts list is often a huge and
time-consuming task. This study proposes a model to automate and expedite the
process by recommending suitable journalists to run media coverage on the press
releases provided by the user.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 13:41:29 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Parekh",
"Soumya",
""
],
[
"Patel",
"Jay",
""
]
] |
new_dataset
| 0.995617 |
2309.00962
|
Jun Zhang
|
Jun Zhang, Huayang Zhuge, Yiyao Liu, Guohao Peng, Zhenyu Wu, Haoyuan
Zhang, Qiyang Lyu, Heshan Li, Chunyang Zhao, Dogan Kircali, Sanat Mharolkar,
Xun Yang, Su Yi, Yuanzhe Wang and Danwei Wang
|
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and
Mapping
|
2023 IEEE International Intelligent Transportation Systems Conference
(ITSC 2023)
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Simultaneous Localization and Mapping (SLAM) is moving towards a robust
perception age. However, LiDAR- and visual- SLAM may easily fail in adverse
conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D
Radar, thermal camera and IMU can work robustly. But only a few literature can
be found. A major reason is the lack of related datasets, which seriously
hinders the research. Even though some datasets are proposed based on 4D radar
in past four years, they are mainly designed for object detection, rather than
SLAM. Furthermore, they normally do not include thermal camera. Therefore, in
this paper, NTU4DRadLM is presented to meet this requirement. The main
characteristics are: 1) It is the only dataset that simultaneously includes all
6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS.
2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth
odometry and intentionally formulated loop closures. 3) Considered both
low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered
structured, unstructured and semi-structured environments. 5) Considered both
middle- and large- scale outdoor environments, i.e., the 6 trajectories range
from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM
algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be
accessible from this link: https://github.com/junzhang2016/NTU4DRadLM
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 15:12:20 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Zhang",
"Jun",
""
],
[
"Zhuge",
"Huayang",
""
],
[
"Liu",
"Yiyao",
""
],
[
"Peng",
"Guohao",
""
],
[
"Wu",
"Zhenyu",
""
],
[
"Zhang",
"Haoyuan",
""
],
[
"Lyu",
"Qiyang",
""
],
[
"Li",
"Heshan",
""
],
[
"Zhao",
"Chunyang",
""
],
[
"Kircali",
"Dogan",
""
],
[
"Mharolkar",
"Sanat",
""
],
[
"Yang",
"Xun",
""
],
[
"Yi",
"Su",
""
],
[
"Wang",
"Yuanzhe",
""
],
[
"Wang",
"Danwei",
""
]
] |
new_dataset
| 0.998235 |
2309.01012
|
Yaxin Hu
|
Yaxin Hu, Hajin Lim, Hailey L. Johnson, Josephine M. O'Shaughnessy,
Lisa Kakonge, Lyn S. Turkstra, Melissa C. Duff, Catalina L. Toma, Bilge Mutlu
|
Investigating the Day-to-Day Experiences of Users with Traumatic Brain
Injury with Conversational Agents
|
In Proceedings The 25th International ACM SIGACCESS Conference on
Computers and Accessibility (ASSETS'23)
| null |
10.1145/3597638.3608385
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traumatic brain injury (TBI) can cause cognitive, communication, and
psychological challenges that profoundly limit independence in everyday life.
Conversational Agents (CAs) can provide individuals with TBI with cognitive and
communication support, although little is known about how they make use of CAs
to address injury-related needs. In this study, we gave nine adults with TBI an
at-home CA for four weeks to investigate use patterns, challenges, and design
requirements, focusing particularly on injury-related use. The findings
revealed significant gaps between the current capabilities of CAs and
accessibility challenges faced by TBI users. We also identified 14 TBI-related
activities that participants engaged in with CAs. We categorized those
activities into four groups: mental health, cognitive activities, healthcare
and rehabilitation, and routine activities. Design implications focus on
accessibility improvements and functional designs of CAs that can better
support the day-to-day needs of people with TBI.
|
[
{
"version": "v1",
"created": "Sat, 2 Sep 2023 20:21:07 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Hu",
"Yaxin",
""
],
[
"Lim",
"Hajin",
""
],
[
"Johnson",
"Hailey L.",
""
],
[
"O'Shaughnessy",
"Josephine M.",
""
],
[
"Kakonge",
"Lisa",
""
],
[
"Turkstra",
"Lyn S.",
""
],
[
"Duff",
"Melissa C.",
""
],
[
"Toma",
"Catalina L.",
""
],
[
"Mutlu",
"Bilge",
""
]
] |
new_dataset
| 0.98701 |
2309.01051
|
Yun Ding
|
Yun Ding, Shixin Zhu, Yang Li
|
On Galois self-orthogonal algebraic geometry codes
|
18papers
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Galois self-orthogonal (SO) codes are generalizations of Euclidean and
Hermitian SO codes. Algebraic geometry (AG) codes are the first known class of
linear codes exceeding the Gilbert-Varshamov bound. Both of them have attracted
much attention for their rich algebraic structures and wide applications in
these years. In this paper, we consider them together and study Galois SO AG
codes. A criterion for an AG code being Galois SO is presented. Based on this
criterion, we construct several new classes of maximum distance separable (MDS)
Galois SO AG codes from projective lines and several new classes of Galois SO
AG codes from projective elliptic curves, hyper-elliptic curves and hermitian
curves. In addition, we give an embedding method that allows us to obtain more
MDS Galois SO codes from known MDS Galois SO AG codes.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 02:03:03 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Ding",
"Yun",
""
],
[
"Zhu",
"Shixin",
""
],
[
"Li",
"Yang",
""
]
] |
new_dataset
| 0.988903 |
2309.01066
|
Surya Karthik Mukkavilli
|
Maximilian Nitsche (1 and 2), S. Karthik Mukkavilli (3), Niklas K\"uhl
(4 and 1), Thomas Brunschwiler (3) ((1) IBM Consulting, Germany, (2)
Karlsruhe Institute of Technology, Germany, (3) IBM Research - Europe,
Switzerland (4) University of Bayreuth, Germany)
|
AB2CD: AI for Building Climate Damage Classification and Detection
|
9 pages, 4 figures
| null | null | null |
cs.CV cs.AI cs.CY eess.IV physics.geo-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We explore the implementation of deep learning techniques for precise
building damage assessment in the context of natural hazards, utilizing remote
sensing data. The xBD dataset, comprising diverse disaster events from across
the globe, serves as the primary focus, facilitating the evaluation of deep
learning models. We tackle the challenges of generalization to novel disasters
and regions while accounting for the influence of low-quality and noisy labels
inherent in natural hazard data. Furthermore, our investigation quantitatively
establishes that the minimum satellite imagery resolution essential for
effective building damage detection is 3 meters and below 1 meter for
classification using symmetric and asymmetric resolution perturbation analyses.
To achieve robust and accurate evaluations of building damage detection and
classification, we evaluated different deep learning models with residual,
squeeze and excitation, and dual path network backbones, as well as ensemble
techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812
performed the best against the xView2 challenge benchmark. Additionally, we
evaluate a Universal model trained on all hazards against a flood expert model
and investigate generalization gaps across events, and out of distribution from
field data in the Ahr Valley. Our research findings showcase the potential and
limitations of advanced AI solutions in enhancing the impact assessment of
climate change-induced extreme weather events, such as floods and hurricanes.
These insights have implications for disaster impact assessment in the face of
escalating climate challenges.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 03:37:04 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Nitsche",
"Maximilian",
"",
"1 and 2"
],
[
"Mukkavilli",
"S. Karthik",
"",
"4 and 1"
],
[
"Kühl",
"Niklas",
"",
"4 and 1"
],
[
"Brunschwiler",
"Thomas",
""
]
] |
new_dataset
| 0.999741 |
2309.01075
|
Xinyue Pan
|
Xinyue Pan, Jiangpeng He, Fengqing Zhu
|
Muti-Stage Hierarchical Food Classification
|
accepted for ACM MM 2023 Madima
| null |
10.1145/3607828.3617798
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Food image classification serves as a fundamental and critical step in
image-based dietary assessment, facilitating nutrient intake analysis from
captured food images. However, existing works in food classification
predominantly focuses on predicting 'food types', which do not contain direct
nutritional composition information. This limitation arises from the inherent
discrepancies in nutrition databases, which are tasked with associating each
'food item' with its respective information. Therefore, in this work we aim to
classify food items to align with nutrition database. To this end, we first
introduce VFN-nutrient dataset by annotating each food image in VFN with a food
item that includes nutritional composition information. Such annotation of food
items, being more discriminative than food types, creates a hierarchical
structure within the dataset. However, since the food item annotations are
solely based on nutritional composition information, they do not always show
visual relations with each other, which poses significant challenges when
applying deep learning-based techniques for classification. To address this
issue, we then propose a multi-stage hierarchical framework for food item
classification by iteratively clustering and merging food items during the
training process, which allows the deep model to extract image features that
are discriminative across labels. Our method is evaluated on VFN-nutrient
dataset and achieve promising results compared with existing work in terms of
both food type and food item classification.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 04:45:44 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Pan",
"Xinyue",
""
],
[
"He",
"Jiangpeng",
""
],
[
"Zhu",
"Fengqing",
""
]
] |
new_dataset
| 0.983577 |
2309.01081
|
Haiyang Yu
|
Haiyang Yu, Xiaocong Wang, Bin Li, Xiangyang Xue
|
Orientation-Independent Chinese Text Recognition in Scene Images
|
IJCAI 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Scene text recognition (STR) has attracted much attention due to its broad
applications. The previous works pay more attention to dealing with the
recognition of Latin text images with complex backgrounds by introducing
language models or other auxiliary networks. Different from Latin texts, many
vertical Chinese texts exist in natural scenes, which brings difficulties to
current state-of-the-art STR methods. In this paper, we take the first attempt
to extract orientation-independent visual features by disentangling content and
orientation information of text images, thus recognizing both horizontal and
vertical texts robustly in natural scenes. Specifically, we introduce a
Character Image Reconstruction Network (CIRN) to recover corresponding printed
character images with disentangled content and orientation information. We
conduct experiments on a scene dataset for benchmarking Chinese text
recognition, and the results demonstrate that the proposed method can indeed
improve performance through disentangling content and orientation information.
To further validate the effectiveness of our method, we additionally collect a
Vertical Chinese Text Recognition (VCTR) dataset. The experimental results show
that the proposed method achieves 45.63% improvement on VCTR when introducing
CIRN to the baseline model.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 05:30:21 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Yu",
"Haiyang",
""
],
[
"Wang",
"Xiaocong",
""
],
[
"Li",
"Bin",
""
],
[
"Xue",
"Xiangyang",
""
]
] |
new_dataset
| 0.99427 |
2309.01083
|
Haiyang Yu
|
Haiyang Yu, Xiaocong Wang, Bin Li, Xiangyang Xue
|
Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through
Image-IDS Aligning
|
ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Scene text recognition has been studied for decades due to its broad
applications. However, despite Chinese characters possessing different
characteristics from Latin characters, such as complex inner structures and
large categories, few methods have been proposed for Chinese Text Recognition
(CTR). Particularly, the characteristic of large categories poses challenges in
dealing with zero-shot and few-shot Chinese characters. In this paper, inspired
by the way humans recognize Chinese texts, we propose a two-stage framework for
CTR. Firstly, we pre-train a CLIP-like model through aligning printed character
images and Ideographic Description Sequences (IDS). This pre-training stage
simulates humans recognizing Chinese characters and obtains the canonical
representation of each character. Subsequently, the learned representations are
employed to supervise the CTR model, such that traditional single-character
recognition can be improved to text-line recognition through image-IDS
matching. To evaluate the effectiveness of the proposed method, we conduct
extensive experiments on both Chinese character recognition (CCR) and CTR. The
experimental results demonstrate that the proposed method performs best in CCR
and outperforms previous methods in most scenarios of the CTR benchmark. It is
worth noting that the proposed method can recognize zero-shot Chinese
characters in text images without fine-tuning, whereas previous methods require
fine-tuning when new classes appear. The code is available at
https://github.com/FudanVI/FudanOCR/tree/main/image-ids-CTR.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 05:33:16 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Yu",
"Haiyang",
""
],
[
"Wang",
"Xiaocong",
""
],
[
"Li",
"Bin",
""
],
[
"Xue",
"Xiangyang",
""
]
] |
new_dataset
| 0.995976 |
2309.01093
|
Jiajin Tang
|
Jiajin Tang, Ge Zheng, Jingyi Yu, Sibei Yang
|
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection
|
Accepted by ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Task driven object detection aims to detect object instances suitable for
affording a task in an image. Its challenge lies in object categories available
for the task being too diverse to be limited to a closed set of object
vocabulary for traditional object detection. Simply mapping categories and
visual features of common objects to the task cannot address the challenge. In
this paper, we propose to explore fundamental affordances rather than object
categories, i.e., common attributes that enable different objects to accomplish
the same task. Moreover, we propose a novel multi-level chain-of-thought
prompting (MLCoT) to extract the affordance knowledge from large language
models, which contains multi-level reasoning steps from task to object examples
to essential visual attributes with rationales. Furthermore, to fully exploit
knowledge to benefit object recognition and localization, we propose a
knowledge-conditional detection framework, namely CoTDet. It conditions the
detector from the knowledge to generate object queries and regress boxes.
Experimental results demonstrate that our CoTDet outperforms state-of-the-art
methods consistently and significantly (+15.6 box AP and +14.8 mask AP) and can
generate rationales for why objects are detected to afford the task.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 06:18:39 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Tang",
"Jiajin",
""
],
[
"Zheng",
"Ge",
""
],
[
"Yu",
"Jingyi",
""
],
[
"Yang",
"Sibei",
""
]
] |
new_dataset
| 0.99732 |
2309.01111
|
Yuhao Du
|
Yuhao Du, Yuncheng Jiang, Shuangyi Tan, Xusheng Wu, Qi Dou, Zhen Li,
Guanbin Li, Xiang Wan
|
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic
Diffusion Models
|
Accepted by MICCAI-2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Colonoscopy analysis, particularly automatic polyp segmentation and
detection, is essential for assisting clinical diagnosis and treatment.
However, as medical image annotation is labour- and resource-intensive, the
scarcity of annotated data limits the effectiveness and generalization of
existing methods. Although recent research has focused on data generation and
augmentation to address this issue, the quality of the generated data remains a
challenge, which limits the contribution to the performance of subsequent
tasks. Inspired by the superiority of diffusion models in fitting data
distributions and generating high-quality data, in this paper, we propose an
Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy
images that benefit the downstream tasks. Specifically, ArSDM utilizes the
ground-truth segmentation mask as a prior condition during training and adjusts
the diffusion loss for each input according to the polyp/background size ratio.
Furthermore, ArSDM incorporates a pre-trained segmentation model to refine the
training process by reducing the difference between the ground-truth mask and
the prediction mask. Extensive experiments on segmentation and detection tasks
demonstrate the generated data by ArSDM could significantly boost the
performance of baseline methods.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 07:55:46 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Du",
"Yuhao",
""
],
[
"Jiang",
"Yuncheng",
""
],
[
"Tan",
"Shuangyi",
""
],
[
"Wu",
"Xusheng",
""
],
[
"Dou",
"Qi",
""
],
[
"Li",
"Zhen",
""
],
[
"Li",
"Guanbin",
""
],
[
"Wan",
"Xiang",
""
]
] |
new_dataset
| 0.969514 |
2309.01112
|
Ze Fu
|
Ze Fu, Yinghui Li, and Weizhong Guo
|
Swing Leg Motion Strategy for Heavy-load Legged Robot Based on Force
Sensing
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The heavy-load legged robot has strong load carrying capacity and can adapt
to various unstructured terrains. But the large weight results in higher
requirements for motion stability and environmental perception ability. In
order to utilize force sensing information to improve its motion performance,
in this paper, we propose a finite state machine model for the swing leg in the
static gait by imitating the movement of the elephant. Based on the presence or
absence of additional terrain information, different trajectory planning
strategies are provided for the swing leg to enhance the success rate of
stepping and save energy. The experimental results on a novel quadruped robot
show that our method has strong robustness and can enable heavy-load legged
robots to pass through various complex terrains autonomously and smoothly.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 08:03:06 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Fu",
"Ze",
""
],
[
"Li",
"Yinghui",
""
],
[
"Guo",
"Weizhong",
""
]
] |
new_dataset
| 0.9951 |
2309.01114
|
Yang Tan
|
Yang Tan, Mingchen Li, Zijie Huang, Huiqun Yu and Guisheng Fan
|
MedChatZH: a Better Medical Adviser Learns from Better Instructions
|
7 pages, 3 figures
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generative large language models (LLMs) have shown great success in various
applications, including question-answering (QA) and dialogue systems. However,
in specialized domains like traditional Chinese medical QA, these models may
perform unsatisfactorily without fine-tuning on domain-specific datasets. To
address this, we introduce MedChatZH, a dialogue model designed specifically
for traditional Chinese medical QA. Our model is pre-trained on Chinese
traditional medical books and fine-tuned with a carefully curated medical
instruction dataset. It outperforms several solid baselines on a real-world
medical dialogue dataset. We release our model, code, and dataset on
https://github.com/tyang816/MedChatZH to facilitate further research in the
domain of traditional Chinese medicine and LLMs.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 08:08:15 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Tan",
"Yang",
""
],
[
"Li",
"Mingchen",
""
],
[
"Huang",
"Zijie",
""
],
[
"Yu",
"Huiqun",
""
],
[
"Fan",
"Guisheng",
""
]
] |
new_dataset
| 0.999354 |
2309.01151
|
Cheng Shi
|
Cheng Shi and Sibei Yang
|
EdaDet: Open-Vocabulary Object Detection Using Early Dense Alignment
|
ICCV 2023; Project Page: https://chengshiest.github.io/edadet
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-language models such as CLIP have boosted the performance of
open-vocabulary object detection, where the detector is trained on base
categories but required to detect novel categories. Existing methods leverage
CLIP's strong zero-shot recognition ability to align object-level embeddings
with textual embeddings of categories. However, we observe that using CLIP for
object-level alignment results in overfitting to base categories, i.e., novel
categories most similar to base categories have particularly poor performance
as they are recognized as similar base categories. In this paper, we first
identify that the loss of critical fine-grained local image semantics hinders
existing methods from attaining strong base-to-novel generalization. Then, we
propose Early Dense Alignment (EDA) to bridge the gap between generalizable
local semantics and object-level prediction. In EDA, we use object-level
supervision to learn the dense-level rather than object-level alignment to
maintain the local fine-grained semantics. Extensive experiments demonstrate
our superior performance to competing approaches under the same strict setting
and without using external training resources, i.e., improving the +8.4% novel
box AP50 on COCO and +3.9% rare mask AP on LVIS.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 12:04:14 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Shi",
"Cheng",
""
],
[
"Yang",
"Sibei",
""
]
] |
new_dataset
| 0.994134 |
2309.01236
|
Dorian F. Henning
|
Dorian F. Henning, Christopher Choi, Simon Schaefer, Stefan
Leutenegger
|
BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human
Motion Tracking
|
IROS 2023. Video: https://youtu.be/UcutiHQwbGk
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robust, fast, and accurate human state - 6D pose and posture - estimation
remains a challenging problem. For real-world applications, the ability to
estimate the human state in real-time is highly desirable. In this paper, we
present BodySLAM++, a fast, efficient, and accurate human and camera state
estimation framework relying on visual-inertial data. BodySLAM++ extends an
existing visual-inertial state estimation framework, OKVIS2, to solve the dual
task of estimating camera and human states simultaneously. Our system improves
the accuracy of both human and camera state estimation with respect to baseline
methods by 26% and 12%, respectively, and achieves real-time performance at 15+
frames per second on an Intel i7-model CPU. Experiments were conducted on a
custom dataset containing both ground truth human and camera poses collected
with an indoor motion tracking system.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 18:09:37 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Henning",
"Dorian F.",
""
],
[
"Choi",
"Christopher",
""
],
[
"Schaefer",
"Simon",
""
],
[
"Leutenegger",
"Stefan",
""
]
] |
new_dataset
| 0.993337 |
2309.01252
|
Dishani Lahiri
|
Dishani Lahiri, Neeraj Panse, Moneish Kumar
|
S2RF: Semantically Stylized Radiance Fields
|
AI for 3D Content Creation at International Conference on Computer
Vision 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present our method for transferring style from any arbitrary image(s) to
object(s) within a 3D scene. Our primary objective is to offer more control in
3D scene stylization, facilitating the creation of customizable and stylized
scene images from arbitrary viewpoints. To achieve this, we propose a novel
approach that incorporates nearest neighborhood-based loss, allowing for
flexible 3D scene reconstruction while effectively capturing intricate style
details and ensuring multi-view consistency.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 19:32:49 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Lahiri",
"Dishani",
""
],
[
"Panse",
"Neeraj",
""
],
[
"Kumar",
"Moneish",
""
]
] |
new_dataset
| 0.998385 |
2309.01279
|
Stefano Puliti
|
Stefano Puliti, Grant Pearse, Peter Surov\'y, Luke Wallace, Markus
Hollaus, Maciej Wielgosz, Rasmus Astrup
|
FOR-instance: a UAV laser scanning benchmark dataset for semantic and
instance segmentation of individual trees
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The FOR-instance dataset (available at
https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate
individual tree segmentation from laser scanning data, crucial for
understanding forest ecosystems and sustainable management. Despite the growing
need for detailed tree data, automating segmentation and tracking scientific
progress remains difficult. Existing methodologies often overfit small datasets
and lack comparability, limiting their applicability. Amid the progress
triggered by the emergence of deep learning methodologies, standardized
benchmarking assumes paramount importance in these research domains. This data
paper introduces a benchmarking dataset for dense airborne laser scanning data,
aimed at advancing instance and semantic segmentation techniques and promoting
progress in 3D forest scene segmentation. The FOR-instance dataset comprises
five curated and ML-ready UAV-based laser scanning data collections from
diverse global locations, representing various forest types. The laser scanning
data were manually annotated into individual trees (instances) and different
semantic classes (e.g. stem, woody branches, live branches, terrain, low
vegetation). The dataset is divided into development and test subsets, enabling
method advancement and evaluation, with specific guidelines for utilization. It
supports instance and semantic segmentation, offering adaptability to deep
learning frameworks and diverse segmentation strategies, while the inclusion of
diameter at breast height data expands its utility to the measurement of a
classic tree variable. In conclusion, the FOR-instance dataset contributes to
filling a gap in the 3D forest research, enhancing the development and
benchmarking of segmentation algorithms for dense airborne laser scanning data.
|
[
{
"version": "v1",
"created": "Sun, 3 Sep 2023 22:08:29 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Puliti",
"Stefano",
""
],
[
"Pearse",
"Grant",
""
],
[
"Surový",
"Peter",
""
],
[
"Wallace",
"Luke",
""
],
[
"Hollaus",
"Markus",
""
],
[
"Wielgosz",
"Maciej",
""
],
[
"Astrup",
"Rasmus",
""
]
] |
new_dataset
| 0.991034 |
2309.01318
|
Gilberto Ochoa-Ruiz
|
Eduardo Guardu\~no-Martinez and Jorge Ciprian-Sanchez and Gerardo
Valente and Vazquez-Garcia and Gerardo Rodriguez-Hernandez and Adriana
Palacios-Rosas and Lucile Rossi-Tisson and Gilberto Ochoa-Ruiz
|
An FPGA smart camera implementation of segmentation models for drone
wildfire imagery
|
This paper has been accepted at the 22nd Mexican International
Conference on Artificial Intelligence (MICAI 2023)
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Wildfires represent one of the most relevant natural disasters worldwide, due
to their impact on various societal and environmental levels. Thus, a
significant amount of research has been carried out to investigate and apply
computer vision techniques to address this problem. One of the most promising
approaches for wildfire fighting is the use of drones equipped with visible and
infrared cameras for the detection, monitoring, and fire spread assessment in a
remote manner but in close proximity to the affected areas. However,
implementing effective computer vision algorithms on board is often prohibitive
since deploying full-precision deep learning models running on GPU is not a
viable option, due to their high power consumption and the limited payload a
drone can handle. Thus, in this work, we posit that smart cameras, based on
low-power consumption field-programmable gate arrays (FPGAs), in tandem with
binarized neural networks (BNNs), represent a cost-effective alternative for
implementing onboard computing on the edge. Herein we present the
implementation of a segmentation model applied to the Corsican Fire Database.
We optimized an existing U-Net model for such a task and ported the model to an
edge device (a Xilinx Ultra96-v2 FPGA). By pruning and quantizing the original
model, we reduce the number of parameters by 90%. Furthermore, additional
optimizations enabled us to increase the throughput of the original model from
8 frames per second (FPS) to 33.63 FPS without loss in the segmentation
performance: our model obtained 0.912 in Matthews correlation coefficient
(MCC),0.915 in F1 score and 0.870 in Hafiane quality index (HAF), and
comparable qualitative segmentation results when contrasted to the original
full-precision model. The final model was integrated into a low-cost FPGA,
which was used to implement a neural network accelerator.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 02:30:14 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Guarduño-Martinez",
"Eduardo",
""
],
[
"Ciprian-Sanchez",
"Jorge",
""
],
[
"Valente",
"Gerardo",
""
],
[
"Vazquez-Garcia",
"",
""
],
[
"Rodriguez-Hernandez",
"Gerardo",
""
],
[
"Palacios-Rosas",
"Adriana",
""
],
[
"Rossi-Tisson",
"Lucile",
""
],
[
"Ochoa-Ruiz",
"Gilberto",
""
]
] |
new_dataset
| 0.993399 |
2309.01324
|
Duo Lu
|
Himanshu Pahadia, Duo Lu, Bharatesh Chakravarthi, Yezhou Yang
|
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from
Traffic Monitoring Cameras
|
Accepted to IEEE ITSC 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Intelligent transportation systems (ITS) have revolutionized modern road
infrastructure, providing essential functionalities such as traffic monitoring,
road safety assessment, congestion reduction, and law enforcement. Effective
vehicle detection and accurate vehicle pose estimation are crucial for ITS,
particularly using monocular cameras installed on the road infrastructure. One
fundamental challenge in vision-based vehicle monitoring is keypoint detection,
which involves identifying and localizing specific points on vehicles (such as
headlights, wheels, taillights, etc.). However, this task is complicated by
vehicle model and shape variations, occlusion, weather, and lighting
conditions. Furthermore, existing traffic perception datasets for keypoint
detection predominantly focus on frontal views from ego vehicle-mounted
sensors, limiting their usability in traffic monitoring. To address these
issues, we propose SKoPe3D, a unique synthetic vehicle keypoint dataset
generated using the CARLA simulator from a roadside perspective. This
comprehensive dataset includes generated images with bounding boxes, tracking
IDs, and 33 keypoints for each vehicle. Spanning over 25k images across 28
scenes, SKoPe3D contains over 150k vehicle instances and 4.9 million keypoints.
To demonstrate its utility, we trained a keypoint R-CNN model on our dataset as
a baseline and conducted a thorough evaluation. Our experiments highlight the
dataset's applicability and the potential for knowledge transfer between
synthetic and real-world data. By leveraging the SKoPe3D dataset, researchers
and practitioners can overcome the limitations of existing datasets, enabling
advancements in vehicle keypoint detection for ITS.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 02:57:30 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Pahadia",
"Himanshu",
""
],
[
"Lu",
"Duo",
""
],
[
"Chakravarthi",
"Bharatesh",
""
],
[
"Yang",
"Yezhou",
""
]
] |
new_dataset
| 0.999845 |
2309.01339
|
Ting-En Lin
|
Zaijing Li, Ting-En Lin, Yuchuan Wu, Meng Liu, Fengxiao Tang, Ming
Zhao, Yongbin Li
|
UniSA: Unified Generative Framework for Sentiment Analysis
|
Accepted to ACM MM 2023
| null | null | null |
cs.CL cs.AI cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Sentiment analysis is a crucial task that aims to understand people's
emotional states and predict emotional categories based on multimodal
information. It consists of several subtasks, such as emotion recognition in
conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal
sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis
presents numerous challenges, including modality alignment, unified
input/output forms, and dataset bias. To address these challenges, we propose a
Task-Specific Prompt method to jointly model subtasks and introduce a
multimodal generative framework called UniSA. Additionally, we organize the
benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation
benchmark, SAEval. We design novel pre-training tasks and training methods to
enable the model to learn generic sentiment knowledge among subtasks to improve
the model's multimodal sentiment perception ability. Our experimental results
show that UniSA performs comparably to the state-of-the-art on all subtasks and
generalizes well to various subtasks in sentiment analysis.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 03:49:30 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Li",
"Zaijing",
""
],
[
"Lin",
"Ting-En",
""
],
[
"Wu",
"Yuchuan",
""
],
[
"Liu",
"Meng",
""
],
[
"Tang",
"Fengxiao",
""
],
[
"Zhao",
"Ming",
""
],
[
"Li",
"Yongbin",
""
]
] |
new_dataset
| 0.999329 |
2309.01346
|
Roshan Vijay
|
James Lee Wei Shung, Paul Hibbard, Roshan Vijay, Lincoln Ang Hon Kin,
Niels de Boer
|
White paper on LiDAR performance against selected Automotive Paints
|
23 pages, 29 figures. This white paper was developed with support
from the Urban Mobility Grand Challenge Fund by the Land Transport Authority
of Singapore (No. UMGC-L010). For associated dataset, see
https://researchdata.ntu.edu.sg/dataset.xhtml?persistentId=doi:10.21979/N9/CGDKMZ
| null | null | null |
cs.RO eess.SP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
LiDAR (Light Detection and Ranging) is a useful sensing technique and an
important source of data for autonomous vehicles (AVs). In this publication we
present the results of a study undertaken to understand the impact of
automotive paint on LiDAR performance along with a methodology used to conduct
this study. Our approach consists of evaluating the average reflected intensity
output by different LiDAR sensor models when tested with different types of
automotive paints. The paints were chosen to represent common paints found on
vehicles in Singapore.
The experiments were conducted with LiDAR sensors commonly used by autonomous
vehicle (AV) developers and OEMs. The paints used were also selected based on
those observed in real-world conditions. This stems from a desire to model
real-world performance of actual sensing systems when exposed to the physical
world. The goal is then to inform regulators of AVs in Singapore of the impact
of automotive paint on LiDAR performance, so that they can determine testing
standards and specifications which will better reflect real-world performance
and also better assess the adequacy of LiDAR systems installed for local AV
operations.
The tests were conducted for a combination of 13 different paint panels and 3
LiDAR sensors. In general, it was observed that darker coloured paints have
lower reflection intensity whereas lighter coloured paints exhibited higher
intensity values.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 04:07:05 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Shung",
"James Lee Wei",
""
],
[
"Hibbard",
"Paul",
""
],
[
"Vijay",
"Roshan",
""
],
[
"Kin",
"Lincoln Ang Hon",
""
],
[
"de Boer",
"Niels",
""
]
] |
new_dataset
| 0.994651 |
2309.01350
|
Manish Bhattarai
|
Maksim E. Eren, Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov,
Charles Nicholas
|
MalwareDNA: Simultaneous Classification of Malware, Malware Families,
and Novel Malware
|
Accepted at IEEE ISI 2023
| null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Malware is one of the most dangerous and costly cyber threats to national
security and a crucial factor in modern cyber-space. However, the adoption of
machine learning (ML) based solutions against malware threats has been
relatively slow. Shortcomings in the existing ML approaches are likely
contributing to this problem. The majority of current ML approaches ignore
real-world challenges such as the detection of novel malware. In addition,
proposed ML approaches are often designed either for malware/benign-ware
classification or malware family classification. Here we introduce and showcase
preliminary capabilities of a new method that can perform precise
identification of novel malware families, while also unifying the capability
for malware/benign-ware classification and malware family classification into a
single framework.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 04:27:39 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Eren",
"Maksim E.",
""
],
[
"Bhattarai",
"Manish",
""
],
[
"Rasmussen",
"Kim",
""
],
[
"Alexandrov",
"Boian S.",
""
],
[
"Nicholas",
"Charles",
""
]
] |
new_dataset
| 0.989313 |
2309.01366
|
Haokun Wen
|
Haokun Wen, Xian Zhang, Xuemeng Song, Yinwei Wei, Liqiang Nie
|
Target-Guided Composed Image Retrieval
| null |
ACM Multimedia 2023
|
10.1145/3581783.3611817
| null |
cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Composed image retrieval (CIR) is a new and flexible image retrieval
paradigm, which can retrieve the target image for a multimodal query, including
a reference image and its corresponding modification text. Although existing
efforts have achieved compelling success, they overlook the conflict
relationship modeling between the reference image and the modification text for
improving the multimodal query composition and the adaptive matching degree
modeling for promoting the ranking of the candidate images that could present
different levels of matching degrees with the given query. To address these two
limitations, in this work, we propose a Target-Guided Composed Image Retrieval
network (TG-CIR). In particular, TG-CIR first extracts the unified global and
local attribute features for the reference/target image and the modification
text with the contrastive language-image pre-training model (CLIP) as the
backbone, where an orthogonal regularization is introduced to promote the
independence among the attribute features. Then TG-CIR designs a target-query
relationship-guided multimodal query composition module, comprising a
target-free student composition branch and a target-based teacher composition
branch, where the target-query relationship is injected into the teacher branch
for guiding the conflict relationship modeling of the student branch. Last,
apart from the conventional batch-based classification loss, TG-CIR
additionally introduces a batch-based target similarity-guided matching degree
regularization to promote the metric learning process. Extensive experiments on
three benchmark datasets demonstrate the superiority of our proposed method.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 05:26:28 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Wen",
"Haokun",
""
],
[
"Zhang",
"Xian",
""
],
[
"Song",
"Xuemeng",
""
],
[
"Wei",
"Yinwei",
""
],
[
"Nie",
"Liqiang",
""
]
] |
new_dataset
| 0.954233 |
2309.01370
|
Monika Jain
|
Monika Jain, Kuldeep Singh, Raghava Mutharaju
|
ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
|
Accepted in ECML 2023
| null | null | null |
cs.CL cs.AI cs.IR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Relation Extraction (RE) is the task of extracting semantic relationships
between entities in a sentence and aligning them to relations defined in a
vocabulary, which is generally in the form of a Knowledge Graph (KG) or an
ontology. Various approaches have been proposed so far to address this task.
However, applying these techniques to biomedical text often yields
unsatisfactory results because it is hard to infer relations directly from
sentences due to the nature of the biomedical relations. To address these
issues, we present a novel technique called ReOnto, that makes use of neuro
symbolic knowledge for the RE task. ReOnto employs a graph neural network to
acquire the sentence representation and leverages publicly accessible
ontologies as prior knowledge to identify the sentential relation between two
entities. The approach involves extracting the relation path between the two
entities from the ontology. We evaluate the effect of using symbolic knowledge
from ontologies with graph neural networks. Experimental results on two public
biomedical datasets, BioRel and ADE, show that our method outperforms all the
baselines (approximately by 3\%).
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 05:36:58 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Jain",
"Monika",
""
],
[
"Singh",
"Kuldeep",
""
],
[
"Mutharaju",
"Raghava",
""
]
] |
new_dataset
| 0.983562 |
2309.01391
|
Burhaneddin Yaman
|
Tanvir Mahmud, Chun-Hao Liu, Burhaneddin Yaman, Diana Marculescu
|
SSVOD: Semi-Supervised Video Object Detection with Sparse Annotations
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite significant progress in semi-supervised learning for image object
detection, several key issues are yet to be addressed for video object
detection: (1) Achieving good performance for supervised video object detection
greatly depends on the availability of annotated frames. (2) Despite having
large inter-frame correlations in a video, collecting annotations for a large
number of frames per video is expensive, time-consuming, and often redundant.
(3) Existing semi-supervised techniques on static images can hardly exploit the
temporal motion dynamics inherently present in videos. In this paper, we
introduce SSVOD, an end-to-end semi-supervised video object detection framework
that exploits motion dynamics of videos to utilize large-scale unlabeled frames
with sparse annotations. To selectively assemble robust pseudo-labels across
groups of frames, we introduce \textit{flow-warped predictions} from nearby
frames for temporal-consistency estimation. In particular, we introduce
cross-IoU and cross-divergence based selection methods over a set of estimated
predictions to include robust pseudo-labels for bounding boxes and class
labels, respectively. To strike a balance between confirmation bias and
uncertainty noise in pseudo-labels, we propose confidence threshold based
combination of hard and soft pseudo-labels. Our method achieves significant
performance improvements over existing methods on ImageNet-VID, Epic-KITCHENS,
and YouTube-VIS datasets. Code and pre-trained models will be released.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 06:41:33 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Mahmud",
"Tanvir",
""
],
[
"Liu",
"Chun-Hao",
""
],
[
"Yaman",
"Burhaneddin",
""
],
[
"Marculescu",
"Diana",
""
]
] |
new_dataset
| 0.994703 |
2309.01399
|
Takeshi Yoshimura
|
Takeshi Yoshimura, Tatsuhiro Chiba, Sunyanan Choochotkaew, Seetharami
Seelam, Hui-fang Wen, Jonas Pfefferle
|
Objcache: An Elastic Filesystem over External Persistent Storage for
Container Clusters
|
13 pages
| null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Container virtualization enables emerging AI workloads such as model serving,
highly parallelized training, machine learning pipelines, and so on, to be
easily scaled on demand on the elastic cloud infrastructure. Particularly, AI
workloads require persistent storage to store data such as training inputs,
models, and checkpoints. An external storage system like cloud object storage
is a common choice because of its elasticity and scalability. To mitigate
access latency to external storage, caching at a local filesystem is an
essential technique. However, building local caches on scaling clusters must
cope with explosive disk usage, redundant networking, and unexpected failures.
We propose objcache, an elastic filesystem over external storage. Objcache
introduces an internal transaction protocol over Raft logging to enable atomic
updates of distributed persistent states with consistent hashing. The proposed
transaction protocol can also manage inode dirtiness by maintaining the
consistency between the local cache and external storage. Objcache supports
scaling down to zero by automatically evicting dirty files to external storage.
Our evaluation reports that objcache speeded up model serving startup by 98.9%
compared to direct copies via S3 interfaces. Scaling up with dirty files
completed from 2 to 14 seconds with 1024 dirty files.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 07:03:28 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Yoshimura",
"Takeshi",
""
],
[
"Chiba",
"Tatsuhiro",
""
],
[
"Choochotkaew",
"Sunyanan",
""
],
[
"Seelam",
"Seetharami",
""
],
[
"Wen",
"Hui-fang",
""
],
[
"Pfefferle",
"Jonas",
""
]
] |
new_dataset
| 0.998408 |
2309.01413
|
Jan Fillies
|
Jan Fillies, Silvio Peikert, Adrian Paschke
|
Hateful Messages: A Conversational Data Set of Hate Speech produced by
Adolescents on Discord
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
With the rise of social media, a rise of hateful content can be observed.
Even though the understanding and definitions of hate speech varies, platforms,
communities, and legislature all acknowledge the problem. Therefore,
adolescents are a new and active group of social media users. The majority of
adolescents experience or witness online hate speech. Research in the field of
automated hate speech classification has been on the rise and focuses on
aspects such as bias, generalizability, and performance. To increase
generalizability and performance, it is important to understand biases within
the data. This research addresses the bias of youth language within hate speech
classification and contributes by providing a modern and anonymized hate speech
youth language data set consisting of 88.395 annotated chat messages. The data
set consists of publicly available online messages from the chat platform
Discord. ~6,42% of the messages were classified by a self-developed annotation
schema as hate speech. For 35.553 messages, the user profiles provided age
annotations setting the average author age to under 20 years old.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 07:48:52 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Fillies",
"Jan",
""
],
[
"Peikert",
"Silvio",
""
],
[
"Paschke",
"Adrian",
""
]
] |
new_dataset
| 0.970441 |
2309.01455
|
Chung-Chi Chen
|
Jian-Tao Huang, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
|
NumHG: A Dataset for Number-Focused Headline Generation
|
NumEval@SemEval-2024 Dataset
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Headline generation, a key task in abstractive summarization, strives to
condense a full-length article into a succinct, single line of text. Notably,
while contemporary encoder-decoder models excel based on the ROUGE metric, they
often falter when it comes to the precise generation of numerals in headlines.
We identify the lack of datasets providing fine-grained annotations for
accurate numeral generation as a major roadblock. To address this, we introduce
a new dataset, the NumHG, and provide over 27,000 annotated numeral-rich news
articles for detailed investigation. Further, we evaluate five well-performing
models from previous headline generation tasks using human evaluation in terms
of numerical accuracy, reasonableness, and readability. Our study reveals a
need for improvement in numerical accuracy, demonstrating the potential of the
NumHG dataset to drive progress in number-focused headline generation and
stimulate further discussions in numeral-focused text generation.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 09:03:53 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Huang",
"Jian-Tao",
""
],
[
"Chen",
"Chung-Chi",
""
],
[
"Huang",
"Hen-Hsen",
""
],
[
"Chen",
"Hsin-Hsi",
""
]
] |
new_dataset
| 0.999847 |
2309.01469
|
Anju Rani
|
Anju Rani and Daniel O. Arroyo and Petar Durdevic
|
Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework
|
12 pages, 7 figures, 4 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Fibre ropes with the latest technology have emerged as an appealing
alternative to steel ropes for offshore industries due to their lightweight and
high tensile strength. At the same time, frequent inspection of these ropes is
essential to ensure the proper functioning and safety of the entire system. The
development of deep learning (DL) models in condition monitoring (CM)
applications offers a simpler and more effective approach for defect detection
in synthetic fibre ropes (SFRs). The present paper investigates the performance
of Detectron2, a state-of-the-art library for defect detection and instance
segmentation. Detectron2 with Mask R-CNN architecture is used for segmenting
defects in SFRs. Mask R-CNN with various backbone configurations has been
trained and tested on an experimentally obtained dataset comprising 1,803
high-dimensional images containing seven damage classes (loop high, loop
medium, loop low, compression, core out, abrasion, and normal respectively) for
SFRs. By leveraging the capabilities of Detectron2, this study aims to develop
an automated and efficient method for detecting defects in SFRs, enhancing the
inspection process, and ensuring the safety of the fibre ropes.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 09:26:04 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Rani",
"Anju",
""
],
[
"Arroyo",
"Daniel O.",
""
],
[
"Durdevic",
"Petar",
""
]
] |
new_dataset
| 0.998675 |
2309.01519
|
Chao Peng
|
Chao Peng, Zhengwei Lv, Jiarong Fu, Jiayuan Liang, Zhao Zhang, Ajitha
Rajan, Ping Yang
|
Hawkeye: Change-targeted Testing for Android Apps based on Deep
Reinforcement Learning
| null | null | null | null |
cs.SE cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Android Apps are frequently updated to keep up with changing user, hardware,
and business demands. Ensuring the correctness of App updates through extensive
testing is crucial to avoid potential bugs reaching the end user. Existing
Android testing tools generate GUI events focussing on improving the test
coverage of the entire App rather than prioritising updates and its impacted
elements. Recent research has proposed change-focused testing but relies on
random exploration to exercise the updates and impacted GUI elements that is
ineffective and slow for large complex Apps with a huge input exploration
space. We propose directed testing of App updates with Hawkeye that is able to
prioritise executing GUI actions associated with code changes based on deep
reinforcement learning from historical exploration data. Our empirical
evaluation compares Hawkeye with state-of-the-art model-based and reinforcement
learning-based testing tools FastBot2 and ARES using 10 popular open-source and
1 commercial App. We find that Hawkeye is able to generate GUI event sequences
targeting changed functions more reliably than FastBot2 and ARES for the open
source Apps and the large commercial App. Hawkeye achieves comparable
performance on smaller open source Apps with a more tractable exploration
space. The industrial deployment of Hawkeye in the development pipeline also
shows that Hawkeye is ideal to perform smoke testing for merge requests of a
complicated commercial App.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 10:57:27 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Peng",
"Chao",
""
],
[
"Lv",
"Zhengwei",
""
],
[
"Fu",
"Jiarong",
""
],
[
"Liang",
"Jiayuan",
""
],
[
"Zhang",
"Zhao",
""
],
[
"Rajan",
"Ajitha",
""
],
[
"Yang",
"Ping",
""
]
] |
new_dataset
| 0.997664 |
2309.01525
|
Laura Piispanen
|
Laura Piispanen, Edward Morrell, Solip Park, Marcell Pfaffhauser,
Annakaisa Kultima
|
The History of Quantum Games
|
8 pages, from which 1.5 pages of references, 11 figures, one table,
presented in the IEEE Conference on Games 2023
| null | null | null |
cs.GL quant-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we explore the historical development of playable quantum
physics related games (\textit{\textbf{quantum games}}). For the purpose of
this examination, we have collected over 260 quantum games ranging from
commercial games, applied and serious games, and games that have been developed
at quantum themed game jams and educational courses. We provide an overview of
the journey of quantum games across three dimensions: \textit{the perceivable
dimension of quantum physics, the dimension of scientific purposes, and the
dimension of quantum technologies}. We then further reflect on the definition
of quantum games and its implications. While motivations behind developing
quantum games have typically been educational or academic, themes related to
quantum physics have begun to be more broadly utilised across a range of
commercial games. In addition, as the availability of quantum computer hardware
has grown, entirely new variants of quantum games have emerged to take
advantage of these machines' inherent capabilities, \textit{quantum computer
games}
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 11:10:58 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Piispanen",
"Laura",
""
],
[
"Morrell",
"Edward",
""
],
[
"Park",
"Solip",
""
],
[
"Pfaffhauser",
"Marcell",
""
],
[
"Kultima",
"Annakaisa",
""
]
] |
new_dataset
| 0.999467 |
2309.01574
|
Henrik Riedel
|
Henik Riedel, Robert Steven Lorenzen and Clemens H\"ubler
|
Raw Data Is All You Need: Virtual Axle Detector with Enhanced Receptive
Field
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Rising maintenance costs of ageing infrastructure necessitate innovative
monitoring techniques. This paper presents a new approach for axle detection,
enabling real-time application of Bridge Weigh-In-Motion (BWIM) systems without
dedicated axle detectors. The proposed method adapts the Virtual Axle Detector
(VAD) model to handle raw acceleration data, which allows the receptive field
to be increased. The proposed Virtual Axle Detector with Enhanced Receptive
field (VADER) improves the \(F_1\) score by 73\% and spatial accuracy by 39\%,
while cutting computational and memory costs by 99\% compared to the
state-of-the-art VAD. VADER reaches a \(F_1\) score of 99.4\% and a spatial
error of 4.13~cm when using a representative training set and functional
sensors. We also introduce a novel receptive field (RF) rule for an object-size
driven design of Convolutional Neural Network (CNN) architectures. Based on
this rule, our results suggest that models using raw data could achieve better
performance than those using spectrograms, offering a compelling reason to
consider raw data as input.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 12:53:54 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Riedel",
"Henik",
""
],
[
"Lorenzen",
"Robert Steven",
""
],
[
"Hübler",
"Clemens",
""
]
] |
new_dataset
| 0.997847 |
2309.01586
|
Matthew Edwards
|
Piyush Bajaj and Matthew Edwards
|
Automatic Scam-Baiting Using ChatGPT
|
Proceedings of the 7th International Workshop on Applications of AI,
Cyber Security and Economics Data Analytics (ACE-2023) (in press)
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Automatic scam-baiting is an online fraud countermeasure that involves
automated systems responding to online fraudsters in order to waste their time
and deplete their resources, diverting attackers away from real potential
victims. Previous work has demonstrated that text generation systems are
capable of engaging with attackers as automatic scam-baiters, but the fluency
and coherence of generated text may be a limit to the effectiveness of such
systems.
In this paper, we report on the results of a month-long experiment comparing
the effectiveness of two ChatGPT-based automatic scam-baiters to a control
measure. Within our results, with engagement from over 250 real email
fraudsters, we find that ChatGPT-based scam-baiters show a marked increase in
scammer response rate and conversation length relative to the control measure,
outperforming previous approaches. We discuss the implications of these results
and practical considerations for wider deployment of automatic scam-baiting.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 13:13:35 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Bajaj",
"Piyush",
""
],
[
"Edwards",
"Matthew",
""
]
] |
new_dataset
| 0.984631 |
2309.01656
|
Vuong Nguyen
|
Vuong Nguyen, Anh Ho, Duc-Anh Vu, Nguyen Thi Ngoc Anh, Tran Ngoc Thang
|
Building Footprint Extraction in Dense Areas using Super Resolution and
Frame Field Learning
|
Accepted at The 12th International Conference on Awareness Science
and Technology
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Despite notable results on standard aerial datasets, current
state-of-the-arts fail to produce accurate building footprints in dense areas
due to challenging properties posed by these areas and limited data
availability. In this paper, we propose a framework to address such issues in
polygonal building extraction. First, super resolution is employed to enhance
the spatial resolution of aerial image, allowing for finer details to be
captured. This enhanced imagery serves as input to a multitask learning module,
which consists of a segmentation head and a frame field learning head to
effectively handle the irregular building structures. Our model is supervised
by adaptive loss weighting, enabling extraction of sharp edges and fine-grained
polygons which is difficult due to overlapping buildings and low data quality.
Extensive experiments on a slum area in India that mimics a dense area
demonstrate that our proposed approach significantly outperforms the current
state-of-the-art methods by a large margin.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 15:15:34 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Nguyen",
"Vuong",
""
],
[
"Ho",
"Anh",
""
],
[
"Vu",
"Duc-Anh",
""
],
[
"Anh",
"Nguyen Thi Ngoc",
""
],
[
"Thang",
"Tran Ngoc",
""
]
] |
new_dataset
| 0.98252 |
2309.01667
|
Tian Qiu
|
Ya-nan Li (1), Tian Qiu (1) and Qiang Tang (1) ((1) The University of
Sydney)
|
Pisces: Private and Compliable Cryptocurrency Exchange
|
27 pages, 8 figures, 2 tables. To be published in NDSS'24. This is
the full version of the conference paper
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cryptocurrency exchange platforms such as Coinbase, Binance, enable users to
purchase and sell cryptocurrencies conveniently just like trading
stocks/commodities. However, because of the nature of blockchain, when a user
withdraws coins (i.e., transfers coins to an external on-chain account), all
future transactions can be learned by the platform. This is in sharp contrast
to conventional stock exchange where all external activities of users are
always hidden from the platform. Since the platform knows highly sensitive user
private information such as passport number, bank information etc, linking all
(on-chain) transactions raises a serious privacy concern about the potential
disastrous data breach in those cryptocurrency exchange platforms.
In this paper, we propose a cryptocurrency exchange that restores user
anonymity for the first time. To our surprise, the seemingly well-studied
privacy/anonymity problem has several new challenges in this setting. Since the
public blockchain and internal transaction activities naturally provide many
non-trivial leakages to the platform, internal privacy is not only useful in
the usual sense but also becomes necessary for regaining the basic anonymity of
user transactions. We also ensure that the user cannot double spend, and the
user has to properly report accumulated profit for tax purposes, even in the
private setting. We give a careful modeling and efficient construction of the
system that achieves constant computation and communication overhead (with only
simple cryptographic tools and rigorous security analysis); we also implement
our system and evaluate its practical performance.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 15:33:46 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Li",
"Ya-nan",
""
],
[
"Qiu",
"Tian",
""
],
[
"Tang",
"Qiang",
""
]
] |
new_dataset
| 0.999272 |
2309.01674
|
Hassan El Hajj
|
Hassan El-Hajj and Matteo Valleriani
|
Prompt me a Dataset: An investigation of text-image prompting for
historical image dataset creation using foundation models
|
12 pages, 3 figures, Accepted in ICIAP2023, AI4DH workshop
| null | null | null |
cs.CV cs.AI cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a pipeline for image extraction from historical
documents using foundation models, and evaluate text-image prompts and their
effectiveness on humanities datasets of varying levels of complexity. The
motivation for this approach stems from the high interest of historians in
visual elements printed alongside historical texts on the one hand, and from
the relative lack of well-annotated datasets within the humanities when
compared to other domains. We propose a sequential approach that relies on
GroundDINO and Meta's Segment-Anything-Model (SAM) to retrieve a significant
portion of visual data from historical documents that can then be used for
downstream development tasks and dataset creation, as well as evaluate the
effect of different linguistic prompts on the resulting detections.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 15:37:03 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"El-Hajj",
"Hassan",
""
],
[
"Valleriani",
"Matteo",
""
]
] |
new_dataset
| 0.999115 |
2309.01775
|
Nicolas Zucchet
|
Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes von Oswald,
Maxime Larcher, Angelika Steger, Jo\~ao Sacramento
|
Gated recurrent neural networks discover attention
| null | null | null | null |
cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent architectural developments have enabled recurrent neural networks
(RNNs) to reach and even surpass the performance of Transformers on certain
sequence modeling tasks. These modern RNNs feature a prominent design pattern:
linear recurrent layers interconnected by feedforward paths with multiplicative
gating. Here, we show how RNNs equipped with these two design elements can
exactly implement (linear) self-attention, the main building block of
Transformers. By reverse-engineering a set of trained RNNs, we find that
gradient descent in practice discovers our construction. In particular, we
examine RNNs trained to solve simple in-context learning tasks on which
Transformers are known to excel and find that gradient descent instills in our
RNNs the same attention-based in-context learning algorithm used by
Transformers. Our findings highlight the importance of multiplicative
interactions in neural networks and suggest that certain RNNs might be
unexpectedly implementing attention under the hood.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 19:28:54 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Zucchet",
"Nicolas",
""
],
[
"Kobayashi",
"Seijin",
""
],
[
"Akram",
"Yassir",
""
],
[
"von Oswald",
"Johannes",
""
],
[
"Larcher",
"Maxime",
""
],
[
"Steger",
"Angelika",
""
],
[
"Sacramento",
"João",
""
]
] |
new_dataset
| 0.995248 |
2309.01798
|
Padmapani Seneviratne
|
Padmapani Seneviratne, Hannah Cuff, Alexandra Koletsos, Kerry Seekamp,
Adrian Thnanopavarn
|
New Qubit Codes from Multidimensional Circulant Graphs
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Two new qubit stabilizer codes with parameters $[77, 0, 19]_2$ and $[90, 0,
22]_2$ are constructed for the first time by employing additive symplectic
self-dual $\F_4$ codes from multidimensional circulant (MDC) graphs. We
completely classify MDC graph codes for lengths $4\le n \le 40$ and show that
many optimal $\dsb{\ell, 0, d}$ qubit codes can be obtained from the MDC
construction. Moreover, we prove that adjacency matrices of MDC graphs have
nested block circulant structure and determine isomorphism properties of MDC
graphs.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 20:24:17 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Seneviratne",
"Padmapani",
""
],
[
"Cuff",
"Hannah",
""
],
[
"Koletsos",
"Alexandra",
""
],
[
"Seekamp",
"Kerry",
""
],
[
"Thnanopavarn",
"Adrian",
""
]
] |
new_dataset
| 0.999357 |
2309.01808
|
Yu-Neng Chuang
|
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen
Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian
Jiang, Xia Hu
|
DiscoverPath: A Knowledge Refinement and Retrieval System for
Interdisciplinarity on Biomedical Research
| null | null | null | null |
cs.IR cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The exponential growth in scholarly publications necessitates advanced tools
for efficient article retrieval, especially in interdisciplinary fields where
diverse terminologies are used to describe similar research. Traditional
keyword-based search engines often fall short in assisting users who may not be
familiar with specific terminologies. To address this, we present a knowledge
graph-based paper search engine for biomedical research to enhance the user
experience in discovering relevant queries and articles. The system, dubbed
DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS)
tagging to extract terminologies and relationships from article abstracts to
create a KG. To reduce information overload, DiscoverPath presents users with a
focused subgraph containing the queried entity and its neighboring nodes and
incorporates a query recommendation system, enabling users to iteratively
refine their queries. The system is equipped with an accessible Graphical User
Interface that provides an intuitive visualization of the KG, query
recommendations, and detailed article information, enabling efficient article
retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath
is open-sourced at https://github.com/ynchuang/DiscoverPath.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 20:52:33 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Chuang",
"Yu-Neng",
""
],
[
"Wang",
"Guanchu",
""
],
[
"Chang",
"Chia-Yuan",
""
],
[
"Lai",
"Kwei-Herng",
""
],
[
"Zha",
"Daochen",
""
],
[
"Tang",
"Ruixiang",
""
],
[
"Yang",
"Fan",
""
],
[
"Reyes",
"Alfredo Costilla",
""
],
[
"Zhou",
"Kaixiong",
""
],
[
"Jiang",
"Xiaoqian",
""
],
[
"Hu",
"Xia",
""
]
] |
new_dataset
| 0.996405 |
2309.01859
|
Alexander Visheratin
|
Alexander Visheratin
|
NLLB-CLIP -- train performant multilingual image retrieval model on a
budget
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Today, the exponential rise of large models developed by academic and
industrial institutions with the help of massive computing resources raises the
question of whether someone without access to such resources can make a
valuable scientific contribution. To explore this, we tried to solve the
challenging task of multilingual image retrieval having a limited budget of
$1,000. As a result, we present NLLB-CLIP - CLIP model with a text encoder from
the NLLB model. To train the model, we used an automatically created dataset of
106,246 good-quality images with captions in 201 languages derived from the
LAION COCO dataset. We trained multiple models using image and text encoders of
various sizes and kept different parts of the model frozen during the training.
We thoroughly analyzed the trained models using existing evaluation datasets
and newly created XTD200 and Flickr30k-200 datasets. We show that NLLB-CLIP is
comparable in quality to state-of-the-art models and significantly outperforms
them on low-resource languages.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 23:26:11 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Visheratin",
"Alexander",
""
]
] |
new_dataset
| 0.998407 |
2309.01861
|
Aashish Gottipati
|
Aashish Gottipati and Jacobus Van der Merwe
|
FlexRDZ: Autonomous Mobility Management for Radio Dynamic Zones
|
This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible
| null | null | null |
cs.NI eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
FlexRDZ is an online, autonomous manager for radio dynamic zones (RDZ) that
seeks to enable the safe operation of RDZs through real-time control of
deployed test transmitters. FlexRDZ leverages Hierarchical Task Networks and
digital twin modeling to plan and resolve RDZ violations in near real-time. We
prototype FlexRDZ with GTPyhop and the Terrain Integrated Rough Earth Model
(TIREM). We deploy and evaluate FlexRDZ within a simulated version of the Salt
Lake City POWDER testbed, a potential urban RDZ environment. Our simulations
show that FlexRDZ enables up to a 20 dBm reduction in mobile interference and a
significant reduction in the total power of leaked transmissions while
preserving the overall communication capabilities and uptime of test
transmitters. To our knowledge, FlexRDZ is the first autonomous system for RDZ
management.
|
[
{
"version": "v1",
"created": "Mon, 4 Sep 2023 23:35:54 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Gottipati",
"Aashish",
""
],
[
"Van der Merwe",
"Jacobus",
""
]
] |
new_dataset
| 0.995812 |
2309.01898
|
Manan Tayal
|
Manan Tayal, Shishir Kolathaya
|
Safe Legged Locomotion using Collision Cone Control Barrier Functions
(C3BFs)
|
5 Pages, 5 Figures. arXiv admin note: text overlap with
arXiv:2303.15871
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Legged robots exhibit significant potential across diverse applications,
including but not limited to hazardous environment search and rescue missions
and the exploration of unexplored regions both on Earth and in outer space.
However, the successful navigation of these robots in dynamic environments
heavily hinges on the implementation of efficient collision avoidance
techniques. In this research paper, we employ Collision Cone Control Barrier
Functions (C3BF) to ensure the secure movement of legged robots within
environments featuring a wide array of static and dynamic obstacles. We
introduce the Quadratic Program (QP) formulation of C3BF, referred to as
C3BF-QP, which serves as a protective filter layer atop a reference controller
to ensure the robots' safety during operation. The effectiveness of this
approach is illustrated through simulations conducted on PyBullet.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 02:15:14 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Tayal",
"Manan",
""
],
[
"Kolathaya",
"Shishir",
""
]
] |
new_dataset
| 0.998618 |
2309.01907
|
Hongruixuan Chen
|
Jian Song and Hongruixuan Chen and Naoto Yokoya
|
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and
Building Change Detection
|
Accepted by WACV 2024
| null | null | null |
cs.CV cs.AI cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Synthetic datasets, recognized for their cost effectiveness, play a pivotal
role in advancing computer vision tasks and techniques. However, when it comes
to remote sensing image processing, the creation of synthetic datasets becomes
challenging due to the demand for larger-scale and more diverse 3D models. This
complexity is compounded by the difficulties associated with real remote
sensing datasets, including limited data acquisition and high annotation costs,
which amplifies the need for high-quality synthetic alternatives. To address
this, we present SyntheWorld, a synthetic dataset unparalleled in quality,
diversity, and scale. It includes 40,000 images with submeter-level pixels and
fine-grained land cover annotations of eight categories, and it also provides
40,000 pairs of bitemporal image pairs with building change annotations for
building change detection task. We conduct experiments on multiple benchmark
remote sensing datasets to verify the effectiveness of SyntheWorld and to
investigate the conditions under which our synthetic data yield advantages. We
will release SyntheWorld to facilitate remote sensing image processing
research.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 02:42:41 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Song",
"Jian",
""
],
[
"Chen",
"Hongruixuan",
""
],
[
"Yokoya",
"Naoto",
""
]
] |
new_dataset
| 0.999751 |
2309.01925
|
Haozhe Wang
|
Lei Zhou, Zhiyang Liu, Runze Gan, Haozhe Wang, Marcelo H. Ang Jr
|
DR-Pose: A Two-stage Deformation-and-Registration Pipeline for
Category-level 6D Object Pose Estimation
|
Camera-ready version accepted to IROS 2023
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Category-level object pose estimation involves estimating the 6D pose and the
3D metric size of objects from predetermined categories. While recent
approaches take categorical shape prior information as reference to improve
pose estimation accuracy, the single-stage network design and training manner
lead to sub-optimal performance since there are two distinct tasks in the
pipeline. In this paper, the advantage of two-stage pipeline over single-stage
design is discussed. To this end, we propose a two-stage deformation-and
registration pipeline called DR-Pose, which consists of completion-aided
deformation stage and scaled registration stage. The first stage uses a point
cloud completion method to generate unseen parts of target object, guiding
subsequent deformation on the shape prior. In the second stage, a novel
registration network is designed to extract pose-sensitive features and predict
the representation of object partial point cloud in canonical space based on
the deformation results from the first stage. DR-Pose produces superior results
to the state-of-the-art shape prior-based methods on both CAMERA25 and REAL275
benchmarks. Codes are available at https://github.com/Zray26/DR-Pose.git.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 03:24:09 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Zhou",
"Lei",
""
],
[
"Liu",
"Zhiyang",
""
],
[
"Gan",
"Runze",
""
],
[
"Wang",
"Haozhe",
""
],
[
"Ang",
"Marcelo H.",
"Jr"
]
] |
new_dataset
| 0.997327 |
2309.01950
|
Dongyeun Lee
|
Dongyeun Lee, Chaewon Kim, Sangjoon Yu, Jaejun Yoo, Gyeong-Moon Park
|
RADIO: Reference-Agnostic Dubbing Video Synthesis
|
Under review
| null | null | null |
cs.CV cs.AI cs.LG cs.SD eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
One of the most challenging problems in audio-driven talking head generation
is achieving high-fidelity detail while ensuring precise synchronization. Given
only a single reference image, extracting meaningful identity attributes
becomes even more challenging, often causing the network to mirror the facial
and lip structures too closely. To address these issues, we introduce RADIO, a
framework engineered to yield high-quality dubbed videos regardless of the pose
or expression in reference images. The key is to modulate the decoder layers
using latent space composed of audio and reference features. Additionally, we
incorporate ViT blocks into the decoder to emphasize high-fidelity details,
especially in the lip region. Our experimental results demonstrate that RADIO
displays high synchronization without the loss of fidelity. Especially in harsh
scenarios where the reference frame deviates significantly from the ground
truth, our method outperforms state-of-the-art methods, highlighting its
robustness. Pre-trained model and codes will be made public after the review.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 04:56:18 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Lee",
"Dongyeun",
""
],
[
"Kim",
"Chaewon",
""
],
[
"Yu",
"Sangjoon",
""
],
[
"Yoo",
"Jaejun",
""
],
[
"Park",
"Gyeong-Moon",
""
]
] |
new_dataset
| 0.964053 |
2309.01954
|
Dibakar Datta
|
Dibakar Datta
|
Electro-Chemo-Mechanical Modeling of Multiscale Active Materials for
Next-Generation Energy Storage: Opportunities and Challenges
|
33 pages, 17 figures
| null | null | null |
cs.CE
|
http://creativecommons.org/licenses/by/4.0/
|
The recent geopolitical crisis resulted in a gas price surge. Although
lithium-ion batteries represent the best available rechargeable battery
technology, a significant energy and power density gap exists between LIBs and
petrol/gasoline. The battery electrodes comprise a mixture of active materials
particles, conductive carbon, and binder additives deposited onto a current
collector. Although this basic design has persisted for decades, the active
material particle's desired size scale is debated. Traditionally,
microparticles have been used in batteries. Advances in nanotechnology have
spurred interest in deploying nanoparticles as active materials. However,
despite many efforts in nano, industries still primarily use 'old'
microparticles. Most importantly, the battery industry is unlikely to replace
microstructures with nanometer-sized analogs. This poses an important question:
Is there a place for nanostructure in battery design due to irreplaceable
microstructure? The way forward lies in multiscale active materials, microscale
structures with built-in nanoscale features, such as microparticles assembled
from nanoscale building blocks or patterned with engineered or natural
nanopores. Although experimental strides have been made in developing such
materials, computational progress in this domain remains limited and, in some
cases, negligible. However, the fields hold immense computational potential,
presenting a multitude of opportunities. This perspective highlights the
existing gaps in modeling multiscale active materials and delineates various
open challenges in the realm of electro-chemo-mechanical modeling. By doing so,
it aims to inspire computational research within this field and promote
synergistic collaborative efforts between computational and experimental
researchers.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 05:06:17 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Datta",
"Dibakar",
""
]
] |
new_dataset
| 0.986546 |
2309.01983
|
Md Ajaharul Hossain
|
Md Ajaharul Hossain, Ramakrishna Bandi
|
Quaternary Conjucyclic Codes with an Application to EAQEC Codes
| null | null | null | null |
cs.IT math.IT math.RA
|
http://creativecommons.org/licenses/by/4.0/
|
Conjucyclic codes are part of a family of codes that includes cyclic,
constacyclic, and quasi-cyclic codes, among others. Despite their importance in
quantum error correction, they have not received much attention in the
literature. This paper focuses on additive conjucyclic (ACC) codes over
$\mathbb{F}_4$ and investigates their properties. Specifically, we derive the
duals of ACC codes using a trace inner product and obtain the trace hull and
its dimension. Also, establish a necessary and sufficient condition for an
additive code to have a complementary dual (ACD). Additionally, we identify a
necessary condition for an additive conjucyclic complementary pair of codes
over $\mathbb{F}_4$. Furthermore, we show that the trace code of an ACC code is
cyclic and provide a condition for the trace code of an ACC code to be LCD. To
demonstrate the practical application of our findings, we construct some good
entanglement-assisted quantum error-correcting (EAQEC) codes using the trace
code of ACC codes.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 06:32:43 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Hossain",
"Md Ajaharul",
""
],
[
"Bandi",
"Ramakrishna",
""
]
] |
new_dataset
| 0.999851 |
2309.01985
|
Md Ajaharul Hossain
|
Md Ajaharul Hossain, Ramakrishna Bandi
|
The $\ell$-intersection Pairs of Constacyclic and Conjucyclic Codes
| null | null | null | null |
cs.IT math.IT math.RA
|
http://creativecommons.org/licenses/by/4.0/
|
A pair of linear codes whose intersection is of dimension $\ell$, where
$\ell$ is a non-negetive integer, is called an $\ell$-intersection pair of
codes. This paper focuses on studying $\ell$-intersection pairs of
$\lambda_i$-constacyclic, $i=1,2,$ and conjucyclic codes. We first characterize
an $\ell$-intersection pair of $\lambda_i$-constacyclic codes. A formula for
$\ell$ has been established in terms of the degrees of the generator
polynomials of $\lambda_i$-constacyclic codes. This allows obtaining a
condition for $\ell$-linear complementary pairs (LPC) of constacyclic codes.
Later, we introduce and characterize the $\ell$-intersection pair of
conjucyclic codes over $\mathbb{F}_{q^2}$. The first observation in the process
is that there are no non-trivial linear conjucyclic codes over finite fields.
So focus on the characterization of additive conjucyclic (ACC) codes. We show
that the largest $\mathbb{F}_q$-subcode of an ACC code over $\mathbb{F}_{q^2}$
is cyclic and obtain its generating polynomial. This enables us to find the
size of an ACC code. Furthermore, we discuss the trace code of an ACC code and
show that it is cyclic. Finally, we determine $\ell$-intersection pairs of
trace codes of ACC codes over $\mathbb{F}_4$.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 06:40:23 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Hossain",
"Md Ajaharul",
""
],
[
"Bandi",
"Ramakrishna",
""
]
] |
new_dataset
| 0.999246 |
2309.02019
|
Nicolas Anquetil
|
Younoussa Sow, Larisa Safina, L\'eandre Brault, Papa Ibou Diouf,
St\'ephane Ducasse, Nicolas Anquetil
|
Parsing Fortran-77 with proprietary extensions
|
Accepted at ICSME'23 Industrial track
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Far from the latest innovations in software development, many organizations
still rely on old code written in "obsolete" programming languages. Because
this source code is old and proven it often contributes significantly to the
continuing success of these organizations. Yet to keep the applications
relevant and running in an evolving environment, they sometimes need to be
updated or migrated to new languages or new platforms. One difficulty of
working with these "veteran languages" is being able to parse the source code
to build a representation of it. Parsing can also allow modern software
development tools and IDEs to offer better support to these veteran languages.
We initiated a project between our group and the Framatome company to help
migrate old Fortran-77 with proprietary extensions (called Esope) into more
modern Fortran. In this paper, we explain how we parsed the Esope language with
a combination of island grammar and regular parser to build an abstract syntax
tree of the code.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 07:54:02 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Sow",
"Younoussa",
""
],
[
"Safina",
"Larisa",
""
],
[
"Brault",
"Léandre",
""
],
[
"Diouf",
"Papa Ibou",
""
],
[
"Ducasse",
"Stéphane",
""
],
[
"Anquetil",
"Nicolas",
""
]
] |
new_dataset
| 0.99154 |
2309.02026
|
Christian Lienen
|
Christian Lienen, Mathis Brede, Daniel Karger, Kevin Koch, Dalisha
Logan, Janet Mazur, Alexander Philipp Nowosad, Alexander Schnelle, Mohness
Waizy and Marco Platzner
|
AutonomROS: A ReconROS-based Autonomonous Driving Unit
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous driving has become an important research area in recent years, and
the corresponding system creates an enormous demand for computations.
Heterogeneous computing platforms such as systems-on-chip that combine CPUs
with reprogrammable hardware offer both computational performance and
flexibility and are thus interesting targets for autonomous driving
architectures. The de-facto software architecture standard in robotics,
including autonomous driving systems, is ROS 2. ReconROS is a framework for
creating robotics applications that extends ROS 2 with the possibility of
mapping compute-intense functions to hardware.
This paper presents AutonomROS, an autonomous driving unit based on the
ReconROS framework. AutonomROS serves as a blueprint for a larger robotics
application developed with ReconROS and demonstrates its suitability and
extendability. The application integrates the ROS 2 package Navigation 2 with
custom-developed software and hardware-accelerated functions for point cloud
generation, obstacle detection, and lane detection. In addition, we detail a
new communication middleware for shared memory communication between software
and hardware functions. We evaluate AutonomROS and show the advantage of
hardware acceleration and the new communication middleware for improving
turnaround times, achievable frame rates, and, most importantly, reducing CPU
load.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 08:12:58 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Lienen",
"Christian",
""
],
[
"Brede",
"Mathis",
""
],
[
"Karger",
"Daniel",
""
],
[
"Koch",
"Kevin",
""
],
[
"Logan",
"Dalisha",
""
],
[
"Mazur",
"Janet",
""
],
[
"Nowosad",
"Alexander Philipp",
""
],
[
"Schnelle",
"Alexander",
""
],
[
"Waizy",
"Mohness",
""
],
[
"Platzner",
"Marco",
""
]
] |
new_dataset
| 0.999445 |
2309.02067
|
Anand Sharma
|
Anand Sharma (MIET, Meerut), A. G. Ramakrishnan (IISc, Bengaluru)
|
Histograms of Points, Orientations, and Dynamics of Orientations
Features for Hindi Online Handwritten Character Recognition
|
21 pages, 12 jpg figures
| null | null | null |
cs.CV eess.SP
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A set of features independent of character stroke direction and order
variations is proposed for online handwritten character recognition. A method
is developed that maps features like co-ordinates of points, orientations of
strokes at points, and dynamics of orientations of strokes at points spatially
as a function of co-ordinate values of the points and computes histograms of
these features from different regions in the spatial map.
Different features like spatio-temporal, discrete Fourier transform, discrete
cosine transform, discrete wavelet transform, spatial, and histograms of
oriented gradients used in other studies for training classifiers for character
recognition are considered. The classifier chosen for classification
performance comparison, when trained with different features, is support vector
machines (SVM).
The character datasets used for training and testing the classifiers consist
of online handwritten samples of 96 different Hindi characters. There are 12832
and 2821 samples in training and testing datasets, respectively.
SVM classifiers trained with the proposed features has the highest
classification accuracy of 92.9\% when compared to the performances of SVM
classifiers trained with the other features and tested on the same testing
dataset. Therefore, the proposed features have better character discriminative
capability than the other features considered for comparison.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 09:11:18 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Sharma",
"Anand",
"",
"MIET, Meerut"
],
[
"Ramakrishnan",
"A. G.",
"",
"IISc, Bengaluru"
]
] |
new_dataset
| 0.996296 |
2309.02102
|
Stephan Alaniz
|
Stephan Alaniz, Massimiliano Mancini, Zeynep Akata
|
Iterative Superquadric Recomposition of 3D Objects from Multiple Views
|
Accepted at ICCV 2023
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humans are good at recomposing novel objects, i.e. they can identify
commonalities between unknown objects from general structure to finer detail,
an ability difficult to replicate by machines. We propose a framework, ISCO, to
recompose an object using 3D superquadrics as semantic parts directly from 2D
views without training a model that uses 3D supervision. To achieve this, we
optimize the superquadric parameters that compose a specific instance of the
object, comparing its rendered 3D view and 2D image silhouette. Our ISCO
framework iteratively adds new superquadrics wherever the reconstruction error
is high, abstracting first coarse regions and then finer details of the target
object. With this simple coarse-to-fine inductive bias, ISCO provides
consistent superquadrics for related object parts, despite not having any
semantic supervision. Since ISCO does not train any neural network, it is also
inherently robust to out-of-distribution objects. Experiments show that,
compared to recent single instance superquadrics reconstruction approaches,
ISCO provides consistently more accurate 3D reconstructions, even from images
in the wild. Code available at https://github.com/ExplainableML/ISCO .
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 10:21:37 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Alaniz",
"Stephan",
""
],
[
"Mancini",
"Massimiliano",
""
],
[
"Akata",
"Zeynep",
""
]
] |
new_dataset
| 0.996061 |
2309.02120
|
Lorenzo Mur-Labadia
|
Lorenzo Mur-Labadia, Jose J. Guerrero and Ruben Martinez-Cantin
|
Multi-label affordance mapping from egocentric vision
|
International Conference on Computer Vision (ICCV) 2023
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate affordance detection and segmentation with pixel precision is an
important piece in many complex systems based on interactions, such as robots
and assitive devices. We present a new approach to affordance perception which
enables accurate multi-label segmentation. Our approach can be used to
automatically extract grounded affordances from first person videos of
interactions using a 3D map of the environment providing pixel level precision
for the affordance location. We use this method to build the largest and most
complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff,
which provides interaction-grounded, multi-label, metric and spatial affordance
annotations. Then, we propose a new approach to affordance segmentation based
on multi-label detection which enables multiple affordances to co-exists in the
same space, for example if they are associated with the same object. We present
several strategies of multi-label detection using several segmentation
architectures. The experimental results highlight the importance of the
multi-label detection. Finally, we show how our metric representation can be
exploited for build a map of interaction hotspots in spatial action-centric
zones and use that representation to perform a task-oriented navigation.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 10:56:23 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Mur-Labadia",
"Lorenzo",
""
],
[
"Guerrero",
"Jose J.",
""
],
[
"Martinez-Cantin",
"Ruben",
""
]
] |
new_dataset
| 0.998711 |
2309.02171
|
Shaoyi Liu
|
Shaoyi Liu, Nan Ma, Yaning Chen, Ke Peng and Dongsheng Xue
|
A Wideband MIMO Channel Model for Aerial Intelligent Reflecting
Surface-Assisted Wireless Communications
|
6 pages, 7 figures
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Compared to traditional intelligent reflecting surfaces(IRS), aerial IRS
(AIRS) has unique advantages, such as more flexible deployment and wider
service coverage. However, modeling AIRS in the channel presents new challenges
due to their mobility. In this paper, a three-dimensional (3D) wideband channel
model for AIRS and IRS joint-assisted multiple-input multiple-output (MIMO)
communication system is proposed, where considering the rotational degrees of
freedom in three directions and the motion angles of AIRS in space. Based on
the proposed model, the channel impulse response (CIR), correlation function,
and channel capacity are derived, and several feasible joint phase shifts
schemes for AIRS and IRS units are proposed. Simulation results show that the
proposed model can capture the channel characteristics accurately, and the
proposed phase shifts methods can effectively improve the channel statistical
characteristics and increase the system capacity. Additionally, we observe that
in certain scenarios, the paths involving the IRS and the line-of-sight (LoS)
paths exhibit similar characteristics. These findings provide valuable insights
for the future development of intelligent communication systems.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 12:21:32 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Liu",
"Shaoyi",
""
],
[
"Ma",
"Nan",
""
],
[
"Chen",
"Yaning",
""
],
[
"Peng",
"Ke",
""
],
[
"Xue",
"Dongsheng",
""
]
] |
new_dataset
| 0.998194 |
2309.02175
|
Tamas David-Barrett
|
Tamas David-Barrett
|
Collaboration Conundrum: Synchrony-Cooperation Trade-off
|
21 pages, 7 figures
| null | null | null |
cs.SI physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In large groups, every collaborative act requires balancing two pressures:
the need to achieve behavioural synchrony and the need to keep free riding to a
minimum. This paper introduces a model of collaboration that requires both
synchronisation on a social network and costly cooperation. The results show
that coordination slows, and cooperativeness increases with the social
network`s local integratedness, measured by the clustering coefficient. That
is, in a large-group collaboration, achieving behavioural synchrony and
strategic cooperation are in opposition to each other. The optimal clustering
coefficient has no natural state in our species, and is determined by the
ecological environment, the group`s technology set, and the group`s size. This
opens the space for social technologies that solve this optimisation problem by
generating optimal social network structures.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 12:27:09 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"David-Barrett",
"Tamas",
""
]
] |
new_dataset
| 0.986855 |
2309.02186
|
Jiaolong Yang
|
Yue Wu, Sicheng Xu, Jianfeng Xiang, Fangyun Wei, Qifeng Chen, Jiaolong
Yang, Xin Tong
|
AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image
Collections
|
SIGGRAPH Asia 2023. Project Page:
https://yuewuhkust.github.io/AniPortraitGAN/
| null | null | null |
cs.CV cs.AI cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Previous animatable 3D-aware GANs for human generation have primarily focused
on either the human head or full body. However, head-only videos are relatively
uncommon in real life, and full body generation typically does not deal with
facial expression control and still has challenges in generating high-quality
results. Towards applicable video avatars, we present an animatable 3D-aware
GAN that generates portrait images with controllable facial expression, head
pose, and shoulder movements. It is a generative model trained on unstructured
2D image collections without using 3D or video data. For the new task, we base
our method on the generative radiance manifold representation and equip it with
learnable facial and head-shoulder deformations. A dual-camera rendering and
adversarial learning scheme is proposed to improve the quality of the generated
faces, which is critical for portrait images. A pose deformation processing
network is developed to generate plausible deformations for challenging regions
such as long hair. Experiments show that our method, trained on unstructured 2D
images, can generate diverse and high-quality 3D portraits with desired control
over different properties.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 12:44:57 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Wu",
"Yue",
""
],
[
"Xu",
"Sicheng",
""
],
[
"Xiang",
"Jianfeng",
""
],
[
"Wei",
"Fangyun",
""
],
[
"Chen",
"Qifeng",
""
],
[
"Yang",
"Jiaolong",
""
],
[
"Tong",
"Xin",
""
]
] |
new_dataset
| 0.99875 |
2309.02221
|
Harrie Passier
|
Arno Broeders and Ruud Hermans and Sylvia Stuurman and Lex Bijlsma and
Harrie Passier
|
Improving students' code correctness and test completeness by informal
specifications
|
14 pages
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The quality of software produced by students is often poor. How to teach
students to develop good quality software has long been a topic in computer
science education and research. We must conclude that we still do not have a
good answer to this question. Specifications are necessary to determine the
correctness of software, to develop error-free software and to write complete
tests. Several attempts have been made to teach students to write
specifications before writing code. So far, that has not proven to be very
successful: Students do not like to write a specification and do not see the
benefits of writing specifications. In this paper we focus on the use of
informal specifications. Instead of teaching students how to write
specifications, we teach them how to use informal specifications to develop
correct software. The results were surprising: the number of errors in software
and the completeness of tests both improved considerably and, most importantly,
students really appreciate the specifications. We think that if students
appreciate specification, we have a key to teach them how to specify and to
appreciate its value.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 13:24:43 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Broeders",
"Arno",
""
],
[
"Hermans",
"Ruud",
""
],
[
"Stuurman",
"Sylvia",
""
],
[
"Bijlsma",
"Lex",
""
],
[
"Passier",
"Harrie",
""
]
] |
new_dataset
| 0.990484 |
2309.02224
|
Wencan Huang
|
Wencan Huang, Daizong Liu, Wei Hu
|
Dense Object Grounding in 3D Scenes
|
ACM MM 2023
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Localizing objects in 3D scenes according to the semantics of a given natural
language is a fundamental yet important task in the field of multimedia
understanding, which benefits various real-world applications such as robotics
and autonomous driving. However, the majority of existing 3D object grounding
methods are restricted to a single-sentence input describing an individual
object, which cannot comprehend and reason more contextualized descriptions of
multiple objects in more practical 3D cases. To this end, we introduce a new
challenging task, called 3D Dense Object Grounding (3D DOG), to jointly
localize multiple objects described in a more complicated paragraph rather than
a single sentence. Instead of naively localizing each sentence-guided object
independently, we found that dense objects described in the same paragraph are
often semantically related and spatially located in a focused region of the 3D
scene. To explore such semantic and spatial relationships of densely referred
objects for more accurate localization, we propose a novel Stacked Transformer
based framework for 3D DOG, named 3DOGSFormer. Specifically, we first devise a
contextual query-driven local transformer decoder to generate initial grounding
proposals for each target object. Then, we employ a proposal-guided global
transformer decoder that exploits the local object features to learn their
correlation for further refining initial grounding proposals. Extensive
experiments on three challenging benchmarks (Nr3D, Sr3D, and ScanRefer) show
that our proposed 3DOGSFormer outperforms state-of-the-art 3D single-object
grounding methods and their dense-object variants by significant margins.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 13:27:19 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Huang",
"Wencan",
""
],
[
"Liu",
"Daizong",
""
],
[
"Hu",
"Wei",
""
]
] |
new_dataset
| 0.958027 |
2309.02230
|
Zhirui Wang Dr
|
Zhechao Wang and Peirui Cheng and Shujing Duan and Kaiqiang Chen and
Zhirui Wang and Xinming Li and Xian Sun
|
DCP-Net: A Distributed Collaborative Perception Network for Remote
Sensing Semantic Segmentation
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Onboard intelligent processing is widely applied in emergency tasks in the
field of remote sensing. However, it is predominantly confined to an individual
platform with a limited observation range as well as susceptibility to
interference, resulting in limited accuracy. Considering the current state of
multi-platform collaborative observation, this article innovatively presents a
distributed collaborative perception network called DCP-Net. Firstly, the
proposed DCP-Net helps members to enhance perception performance by integrating
features from other platforms. Secondly, a self-mutual information match module
is proposed to identify collaboration opportunities and select suitable
partners, prioritizing critical collaborative features and reducing redundant
transmission cost. Thirdly, a related feature fusion module is designed to
address the misalignment between local and collaborative features, improving
the quality of fused features for the downstream task. We conduct extensive
experiments and visualization analyses using three semantic segmentation
datasets, including Potsdam, iSAID and DFC23. The results demonstrate that
DCP-Net outperforms the existing methods comprehensively, improving mIoU by
2.61%~16.89% at the highest collaboration efficiency, which promotes the
performance to a state-of-the-art level.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 13:36:40 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Wang",
"Zhechao",
""
],
[
"Cheng",
"Peirui",
""
],
[
"Duan",
"Shujing",
""
],
[
"Chen",
"Kaiqiang",
""
],
[
"Wang",
"Zhirui",
""
],
[
"Li",
"Xinming",
""
],
[
"Sun",
"Xian",
""
]
] |
new_dataset
| 0.987784 |
2309.02253
|
Lucas Correia
|
Lucas Correia, Jan-Christoph Goos, Philipp Klein, Thomas B\"ack, Anna
V. Kononova
|
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for
Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance
Powertrain Testing
|
Accepted in NCTA2023
| null | null | null |
cs.LG cs.AI cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
A clear need for automatic anomaly detection applied to automotive testing
has emerged as more and more attention is paid to the data recorded and manual
evaluation by humans reaches its capacity. Such real-world data is massive,
diverse, multivariate and temporal in nature, therefore requiring modelling of
the testee behaviour. We propose a variational autoencoder with multi-head
attention (MA-VAE), which, when trained on unlabelled data, not only provides
very few false positives but also manages to detect the majority of the
anomalies presented. In addition to that, the approach offers a novel way to
avoid the bypass phenomenon, an undesirable behaviour investigated in
literature. Lastly, the approach also introduces a new method to remap
individual windows to a continuous time series. The results are presented in
the context of a real-world industrial data set and several experiments are
undertaken to further investigate certain aspects of the proposed model. When
configured properly, it is 9% of the time wrong when an anomaly is flagged and
discovers 67% of the anomalies present. Also, MA-VAE has the potential to
perform well with only a fraction of the training and validation subset,
however, to extract it, a more sophisticated threshold estimation method is
required.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:05:37 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Correia",
"Lucas",
""
],
[
"Goos",
"Jan-Christoph",
""
],
[
"Klein",
"Philipp",
""
],
[
"Bäck",
"Thomas",
""
],
[
"Kononova",
"Anna V.",
""
]
] |
new_dataset
| 0.994817 |
2309.02255
|
Damien Courouss\'e
|
Thomas Chamelot and Damien Courouss\'e and Karine Heydemann
|
MAFIA: Protecting the Microarchitecture of Embedded Systems Against
Fault Injection Attacks
|
published by IEEE TCAD
|
IEEE TCAD (2023)
|
10.1109/TCAD.2023.3276507
| null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Fault injection attacks represent an effective threat to embedded systems.
Recently, Laurent et al. have reported that fault injection attacks can
leverage faults inside the microarchitecture. However, state-of-the-art
counter-measures, hardwareonly or with hardware support, do not consider the
integrity of microarchitecture control signals that are the target of these
faults.
We present MAFIA, a microarchitecture protection against fault injection
attacks. MAFIA ensures integrity of pipeline control signals through a
signature-based mechanism, and ensures fine-grained control-flow integrity with
a complete indirect branch support and code authenticity. We analyse the
security properties of two different implementations with different
security/overhead trade-offs: one with a CBC-MAC/Prince signature function, and
another one with a CRC32. We present our implementation of MAFIA in a RISC-V
processor, supported by a dedicated compiler toolchain based on LLVM/Clang. We
report a hardware area overhead of 23.8 % and 6.5 % for the CBC-MAC/Prince and
CRC32 respectively. The average code size and execution time overheads are 29.4
% and 18.4 % respectively for the CRC32 implementation and are 50 % and 39 %
for the CBC-MAC/Prince.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:08:36 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Chamelot",
"Thomas",
""
],
[
"Couroussé",
"Damien",
""
],
[
"Heydemann",
"Karine",
""
]
] |
new_dataset
| 0.998364 |
2309.02258
|
Patricia Bachmann
|
Patricia Bachmann, Ignaz Rutter, Peter Stumpf
|
On 3-Coloring Circle Graphs
|
Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023)
| null | null | null |
cs.DM cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Given a graph $G$ with a fixed vertex order $\prec$, one obtains a circle
graph $H$ whose vertices are the edges of $G$ and where two such edges are
adjacent if and only if their endpoints are pairwise distinct and alternate in
$\prec$. Therefore, the problem of determining whether $G$ has a $k$-page book
embedding with spine order $\prec$ is equivalent to deciding whether $H$ can be
colored with $k$ colors. Finding a $k$-coloring for a circle graph is known to
be NP-complete for $k \geq 4$ and trivial for $k \leq 2$. For $k = 3$, Unger
(1992) claims an efficient algorithm that finds a 3-coloring in $O(n \log n)$
time, if it exists. Given a circle graph $H$, Unger's algorithm (1) constructs
a 3-\textsc{Sat} formula $\Phi$ that is satisfiable if and only if $H$ admits a
3-coloring and (2) solves $\Phi$ by a backtracking strategy that relies on the
structure imposed by the circle graph. However, the extended abstract misses
several details and Unger refers to his PhD thesis (in German) for details. In
this paper we argue that Unger's algorithm for 3-coloring circle graphs is not
correct and that 3-coloring circle graphs should be considered as an open
problem. We show that step (1) of Unger's algorithm is incorrect by exhibiting
a circle graph whose formula $\Phi$ is satisfiable but that is not 3-colorable.
We further show that Unger's backtracking strategy for solving $\Phi$ in step
(2) may produce incorrect results and give empirical evidence that it exhibits
a runtime behaviour that is not consistent with the claimed running time.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:11:29 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Bachmann",
"Patricia",
""
],
[
"Rutter",
"Ignaz",
""
],
[
"Stumpf",
"Peter",
""
]
] |
new_dataset
| 0.998084 |
2309.02259
|
Ruipeng Yang
|
Ruipeng Yang, Yi Fang, Pingping Chen, and Huan Ma
|
Design of a New CIM-DCSK-Based Ambient Backscatter Communication System
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To improve the data rate in differential chaos shift keying (DCSK) based
ambient backscatter communication (AmBC) system, we propose a new AmBC system
based on code index modulation (CIM), referred to as CIM-DCSK-AmBC system. In
the proposed system, the CIM-DCSK signal transmitted in the direct link is used
as the radio frequency source of the backscatter link. The signal format in the
backscatter link is designed to increase the data rate as well as eliminate the
interference of the direct link signal. As such, the direct link signal and the
backscatter link signal can be received and demodulated simultaneously.
Moreover, we derive and validate the theoretical bit error rate (BER)
expressions of the CIM-DCSK-AmBC system over multipath Rayleigh fading
channels. Regarding the short reference DCSK-based AmBC (SR-DCSK-AmBC) system
as a benchmark system, numerical results reveal that the CIM-DCSK-AmBC system
can achieve better BER performance in the direct link and higher throughput in
the backscatter link than the benchmark system.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:12:14 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Yang",
"Ruipeng",
""
],
[
"Fang",
"Yi",
""
],
[
"Chen",
"Pingping",
""
],
[
"Ma",
"Huan",
""
]
] |
new_dataset
| 0.991258 |
2309.02273
|
Markus Wallinger
|
Martin N\"ollenburg and Markus Wallinger
|
Computing Hive Plots: A Combinatorial Framework
|
Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023)
| null | null | null |
cs.CG cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Hive plots are a graph visualization style placing vertices on a set of
radial axes emanating from a common center and drawing edges as smooth curves
connecting their respective endpoints. In previous work on hive plots,
assignment to an axis and vertex positions on each axis were determined based
on selected vertex attributes and the order of axes was prespecified. Here, we
present a new framework focusing on combinatorial aspects of these drawings to
extend the original hive plot idea and optimize visual properties such as the
total edge length and the number of edge crossings in the resulting hive plots.
Our framework comprises three steps: (1) partition the vertices into multiple
groups, each corresponding to an axis of the hive plot; (2) optimize the cyclic
axis order to bring more strongly connected groups near each other; (3)
optimize the vertex ordering on each axis to minimize edge crossings. Each of
the three steps is related to a well-studied, but NP-complete computational
problem. We combine and adapt suitable algorithmic approaches, implement them
as an instantiation of our framework and show in a case study how it can be
applied in a practical setting. Furthermore, we conduct computational
experiments to gain further insights regarding algorithmic choices of the
framework. The code of the implementation and a prototype web application can
be found on OSF.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:37:59 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Nöllenburg",
"Martin",
""
],
[
"Wallinger",
"Markus",
""
]
] |
new_dataset
| 0.996319 |
2309.02286
|
Julian Lorenz
|
Julian Lorenz, Florian Barthel, Daniel Kienzle, Rainer Lienhart
|
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate
Classes
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current scene graph datasets suffer from strong long-tail distributions of
their predicate classes. Due to a very low number of some predicate classes in
the test sets, no reliable metrics can be retrieved for the rarest classes. We
construct a new panoptic scene graph dataset and a set of metrics that are
designed as a benchmark for the predictive performance especially on rare
predicate classes. To construct the new dataset, we propose a model-assisted
annotation pipeline that efficiently finds rare predicate classes that are
hidden in a large set of images like needles in a haystack.
Contrary to prior scene graph datasets, Haystack contains explicit negative
annotations, i.e. annotations that a given relation does not have a certain
predicate class. Negative annotations are helpful especially in the field of
scene graph generation and open up a whole new set of possibilities to improve
current scene graph generation models.
Haystack is 100% compatible with existing panoptic scene graph datasets and
can easily be integrated with existing evaluation pipelines. Our dataset and
code can be found here: https://lorjul.github.io/haystack/. It includes
annotation files and simple to use scripts and utilities, to help with
integrating our dataset in existing work.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 14:45:54 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Lorenz",
"Julian",
""
],
[
"Barthel",
"Florian",
""
],
[
"Kienzle",
"Daniel",
""
],
[
"Lienhart",
"Rainer",
""
]
] |
new_dataset
| 0.998781 |
2309.02340
|
Alhasan Abdellatif
|
Alhasan Abdellatif and Ahmed H. Elsheikh
|
Generating Infinite-Resolution Texture using GANs with Patch-by-Patch
Paradigm
| null | null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce a novel approach for generating texture images of
infinite resolutions using Generative Adversarial Networks (GANs) based on a
patch-by-patch paradigm. Existing texture synthesis techniques often rely on
generating a large-scale texture using a one-forward pass to the generating
model, this limits the scalability and flexibility of the generated images. In
contrast, the proposed approach trains GANs models on a single texture image to
generate relatively small patches that are locally correlated and can be
seamlessly concatenated to form a larger image while using a constant GPU
memory footprint. Our method learns the local texture structure and is able to
generate arbitrary-size textures, while also maintaining coherence and
diversity. The proposed method relies on local padding in the generator to
ensure consistency between patches and utilizes spatial stochastic modulation
to allow for local variations and diversity within the large-scale image.
Experimental results demonstrate superior scalability compared to existing
approaches while maintaining visual coherence of generated textures.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 15:57:23 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Abdellatif",
"Alhasan",
""
],
[
"Elsheikh",
"Ahmed H.",
""
]
] |
new_dataset
| 0.963045 |
2309.02367
|
Tiziano Dalmonte
|
Tiziano Dalmonte
|
Minimal modal logics, constructive modal logics and their relations
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
We present a family of minimal modal logics (namely, modal logics based on
minimal propositional logic) corresponding each to a different classical modal
logic. The minimal modal logics are defined based on their classical
counterparts in two distinct ways: (1) via embedding into fusions of classical
modal logics through a natural extension of the G\"odel-Johansson translation
of minimal logic into modal logic S4; (2) via extension to modal logics of the
multi- vs. single-succedent correspondence of sequent calculi for classical and
minimal logic. We show that, despite being mutually independent, the two
methods turn out to be equivalent for a wide class of modal systems. Moreover,
we compare the resulting minimal version of K with the constructive modal logic
CK studied in the literature, displaying tight relations among the two systems.
Based on these relations, we also define a constructive correspondent for each
minimal system, thus obtaining a family of constructive modal logics which
includes CK as well as other constructive modal logics studied in the
literature.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 16:29:34 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Dalmonte",
"Tiziano",
""
]
] |
new_dataset
| 0.999099 |
2309.02394
|
Natalia Pavlasek
|
Natalia Pavlasek, Charles Champagne Cossette, David Roy-Guay, James
Richard Forbes
|
Magnetic Navigation using Attitude-Invariant Magnetic Field Information
for Loop Closure Detection
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Indoor magnetic fields are a combination of Earth's magnetic field and
disruptions induced by ferromagnetic objects, such as steel structural
components in buildings. As a result of these disruptions, pervasive in indoor
spaces, magnetic field data is often omitted from navigation algorithms in
indoor environments. This paper leverages the spatially-varying disruptions to
Earth's magnetic field to extract positional information for use in indoor
navigation algorithms. The algorithm uses a rate gyro and an array of four
magnetometers to estimate the robot's pose. Additionally, the magnetometer
array is used to compute attitude-invariant measurements associated with the
magnetic field and its gradient. These measurements are used to detect loop
closure points. Experimental results indicate that the proposed approach can
estimate the pose of a ground robot in an indoor environment within meter
accuracy.
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 17:05:16 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"Pavlasek",
"Natalia",
""
],
[
"Cossette",
"Charles Champagne",
""
],
[
"Roy-Guay",
"David",
""
],
[
"Forbes",
"James Richard",
""
]
] |
new_dataset
| 0.955032 |
2309.02401
|
Nanne van Noord
|
Nanne van Noord
|
Prototype-based Dataset Comparison
|
To be presented at ICCV 2023
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Dataset summarisation is a fruitful approach to dataset inspection. However,
when applied to a single dataset the discovery of visual concepts is restricted
to those most prominent. We argue that a comparative approach can expand upon
this paradigm to enable richer forms of dataset inspection that go beyond the
most prominent concepts. To enable dataset comparison we present a module that
learns concept-level prototypes across datasets. We leverage self-supervised
learning to discover these prototypes without supervision, and we demonstrate
the benefits of our approach in two case-studies. Our findings show that
dataset comparison extends dataset inspection and we hope to encourage more
works in this direction. Code and usage instructions available at
https://github.com/Nanne/ProtoSim
|
[
{
"version": "v1",
"created": "Tue, 5 Sep 2023 17:27:16 GMT"
}
] | 2023-09-06T00:00:00 |
[
[
"van Noord",
"Nanne",
""
]
] |
new_dataset
| 0.999158 |
2008.06465
|
Ugur Kursuncu
|
Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie
L. Shalin, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar
|
ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
|
Accepted: Social Informatics 2020
|
International Conference on Social Informatics. 12467 (2020)
427-439
|
10.1007/978-3-030-60975-7_31
| null |
cs.SI cs.CY cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The convenience of social media has also enabled its misuse, potentially
resulting in toxic behavior. Nearly 66% of internet users have observed online
harassment, and 41% claim personal experience, with 18% facing severe forms of
online harassment. This toxic communication has a significant impact on the
well-being of young individuals, affecting mental health and, in some cases,
resulting in suicide. These communications exhibit complex linguistic and
contextual characteristics, making recognition of such narratives challenging.
In this paper, we provide a multimodal dataset of toxic social media
interactions between confirmed high school students, called ALONE (AdoLescents
ON twittEr), along with descriptive explanation. Each instance of interaction
includes tweets, images, emoji and related metadata. Our observations show that
individual tweets do not provide sufficient evidence for toxic behavior, and
meaningful use of context in interactions can enable highlighting or
exonerating tweets with purported toxicity.
|
[
{
"version": "v1",
"created": "Fri, 14 Aug 2020 17:02:55 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Wijesiriwardene",
"Thilini",
""
],
[
"Inan",
"Hale",
""
],
[
"Kursuncu",
"Ugur",
""
],
[
"Gaur",
"Manas",
""
],
[
"Shalin",
"Valerie L.",
""
],
[
"Thirunarayan",
"Krishnaprasad",
""
],
[
"Sheth",
"Amit",
""
],
[
"Arpinar",
"I. Budak",
""
]
] |
new_dataset
| 0.99969 |
2208.00487
|
Aravind Battaje
|
Aravind Battaje, Oliver Brock
|
One Object at a Time: Accurate and Robust Structure From Motion for
Robots
|
v3: Add link to project page v2: Update DOI v1: Accepted at 2022
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
| null |
10.1109/IROS47612.2022.9981953
| null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A gaze-fixating robot perceives distance to the fixated object and relative
positions of surrounding objects immediately, accurately, and robustly. We show
how fixation, which is the act of looking at one object while moving, exploits
regularities in the geometry of 3D space to obtain this information. These
regularities introduce rotation-translation couplings that are not commonly
used in structure from motion. To validate, we use a Franka Emika Robot with an
RGB camera. We a) find that error in distance estimate is less than 5 mm at a
distance of 15 cm, and b) show how relative position can be used to find
obstacles under challenging scenarios. We combine accurate distance estimates
and obstacle information into a reactive robot behavior that is able to pick up
objects of unknown size, while impeded by unforeseen obstacles. Project page:
https://oxidification.com/p/one-object-at-a-time/ .
|
[
{
"version": "v1",
"created": "Sun, 31 Jul 2022 18:17:04 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Jan 2023 13:07:45 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Sep 2023 14:02:16 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Battaje",
"Aravind",
""
],
[
"Brock",
"Oliver",
""
]
] |
new_dataset
| 0.96833 |
2210.11299
|
Nikolaos Athanasios Anagnostopoulos
|
Emiliia Nazarenko, Nikolaos Athanasios Anagnostopoulos, Stavros G.
Stavrinides, Nico Mexis, Florian Frank, Tolga Arul, Stefan Katzenbeisser
|
Real-World Chaos-Based Cryptography Using Synchronised Chua Chaotic
Circuits
|
This work was accepted for and presented as a hardware demo at the
2022 IEEE International Symposium on Hardware Oriented Security and Trust
(HOST 2022), held from 27 to 30 June 2022, in Washington, DC, USA
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
This work presents the hardware demonstrator of a secure encryption system
based on synchronised Chua chaotic circuits. In particular, the presented
encryption system comprises two Chua circuits that are synchronised using a
dedicated bidirectional synchronisation line. One of them forms part of the
transmitter, while the other of the receiver. Both circuits are tuned to
operate in a chaotic mode. The output (chaotic) signal of the first circuit
(transmitter) is digitised and then combined with the message to be encrypted,
through an XOR gate. The second Chua circuit (receiver) is used for the
decryption; the output chaotic signal of this circuit is similarly digitised
and combined with the encrypted message to retrieve the original message. Our
hardware demonstrator proves that this method can be used in order to provide
extremely lightweight real-world, chaos-based cryptographic solutions.
|
[
{
"version": "v1",
"created": "Fri, 12 Aug 2022 00:42:42 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jul 2023 16:12:19 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Nazarenko",
"Emiliia",
""
],
[
"Anagnostopoulos",
"Nikolaos Athanasios",
""
],
[
"Stavrinides",
"Stavros G.",
""
],
[
"Mexis",
"Nico",
""
],
[
"Frank",
"Florian",
""
],
[
"Arul",
"Tolga",
""
],
[
"Katzenbeisser",
"Stefan",
""
]
] |
new_dataset
| 0.999649 |
2211.13854
|
Xuehai He
|
Kenan Jiang, Xuehai He, Ruize Xu, Xin Eric Wang
|
ComCLIP: Training-Free Compositional Image and Text Matching
| null | null | null | null |
cs.CV cs.AI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Contrastive Language-Image Pretraining (CLIP) has demonstrated great
zero-shot performance for matching images and text. However, it is still
challenging to adapt vision-lanaguage pretrained models like CLIP to
compositional image and text matching -- a more challenging image and text
matching task requiring the model understanding of compositional word concepts
and visual components. Towards better compositional generalization in zero-shot
image and text matching, in this paper, we study the problem from a causal
perspective: the erroneous semantics of individual entities are essentially
confounders that cause the matching failure. Therefore, we propose a novel
\textbf{\textit{training-free}} compositional CLIP model (ComCLIP). ComCLIP
disentangles input images into subjects, objects, and action sub-images and
composes CLIP's vision encoder and text encoder to perform evolving matching
over compositional text embedding and sub-image embeddings. In this way,
ComCLIP can mitigate spurious correlations introduced by the pretrained CLIP
models and dynamically evaluate the importance of each component. Experiments
on four compositional image-text matching datasets: SVO, ComVG, Winoground, and
VL-checklist, and two general image-text retrieval datasets: Flick30K, and
MSCOCO demonstrate the effectiveness of our plug-and-play method, which boosts
the \textbf{\textit{zero-shot}} inference ability of CLIP, SLIP, and BLIP2 even
without further training or fine-tuning.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 01:37:48 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 05:07:18 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Jiang",
"Kenan",
""
],
[
"He",
"Xuehai",
""
],
[
"Xu",
"Ruize",
""
],
[
"Wang",
"Xin Eric",
""
]
] |
new_dataset
| 0.973415 |
2212.01691
|
Shathushan Sivashangaran
|
Shathushan Sivashangaran and Azim Eskandarian
|
XTENTH-CAR: A Proportionally Scaled Experimental Vehicle Platform for
Connected Autonomy and All-Terrain Research
|
$\copyright$ 2023 ASME. This work has been accepted to ASME for
publication
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Connected Autonomous Vehicles (CAVs) are key components of the Intelligent
Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs)
are indispensable tools for a wide range of applications such as disaster
response, automated mining, agriculture, military operations, search and rescue
missions, and planetary exploration. Experimental validation is a requisite for
CAV and AGV research, but requires a large, safe experimental environment when
using full-size vehicles which is time-consuming and expensive. To address
these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled
vehicle platform for Connected autonomy and All-terrain Research), an
open-source, cost-effective proportionally one-tenth scaled experimental
vehicle platform governed by the same physics as a full-size on-road vehicle.
XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on
Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed
Controller (ESC) with drivers written for both versions of the Robot Operating
System (ROS 1 & ROS 2) to facilitate experimental CAV and AGV perception,
motion planning and control research, that incorporate state-of-the-art
computationally expensive algorithms such as Deep Reinforcement Learning (DRL).
XTENTH-CAR is designed for compact experimental environments, and aims to
increase the accessibility of experimental CAV and AGV research with low
upfront costs, and complete Autonomous Vehicle (AV) hardware and software
architectures similar to the full-sized X-CAR experimental vehicle platform,
enabling efficient cross-platform development between small-scale and
full-scale vehicles.
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 21:00:41 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 03:10:26 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Sivashangaran",
"Shathushan",
""
],
[
"Eskandarian",
"Azim",
""
]
] |
new_dataset
| 0.997585 |
2304.02013
|
Shih-Yang Su
|
Shih-Yang Su, Timur Bagautdinov, Helge Rhodin
|
NPC: Neural Point Characters from Video
|
Project website: https://lemonatsu.github.io/npc/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
High-fidelity human 3D models can now be learned directly from videos,
typically by combining a template-based surface model with neural
representations. However, obtaining a template surface requires expensive
multi-view capture systems, laser scans, or strictly controlled conditions.
Previous methods avoid using a template but rely on a costly or ill-posed
mapping from observation to canonical space. We propose a hybrid point-based
representation for reconstructing animatable characters that does not require
an explicit surface model, while being generalizable to novel poses. For a
given video, our method automatically produces an explicit set of 3D points
representing approximate canonical geometry, and learns an articulated
deformation model that produces pose-dependent point transformations. The
points serve both as a scaffold for high-frequency neural features and an
anchor for efficiently mapping between observation and canonical space. We
demonstrate on established benchmarks that our representation overcomes
limitations of prior work operating in either canonical or in observation
space. Moreover, our automatic point extraction approach enables learning
models of human and animal characters alike, matching the performance of the
methods using rigged surface templates despite being more general. Project
website: https://lemonatsu.github.io/npc/
|
[
{
"version": "v1",
"created": "Tue, 4 Apr 2023 17:59:22 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 04:20:25 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Su",
"Shih-Yang",
""
],
[
"Bagautdinov",
"Timur",
""
],
[
"Rhodin",
"Helge",
""
]
] |
new_dataset
| 0.965258 |
2304.02216
|
Zilong Zhang
|
Zilong Zhang, Zhibin Zhao, Xingwu Zhang, Chuang Sun, Xuefeng Chen
|
Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and
Masked Multi-scale Reconstruction
|
Accept by Computers in Industry
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Industrial anomaly detection (IAD) is crucial for automating industrial
quality inspection. The diversity of the datasets is the foundation for
developing comprehensive IAD algorithms. Existing IAD datasets focus on the
diversity of data categories, overlooking the diversity of domains within the
same data category. In this paper, to bridge this gap, we propose the
Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two
sub-datasets: the single-blade dataset and the video anomaly detection dataset
of blades. Compared to existing datasets, AeBAD has the following two
characteristics: 1.) The target samples are not aligned and at different
scales. 2.) There is a domain shift between the distribution of normal samples
in the test set and the training set, where the domain shifts are mainly caused
by the changes in illumination and view. Based on this dataset, we observe that
current state-of-the-art (SOTA) IAD methods exhibit limitations when the domain
of normal samples in the test set undergoes a shift. To address this issue, we
propose a novel method called masked multi-scale reconstruction (MMR), which
enhances the model's capacity to deduce causality among patches in normal
samples by a masked reconstruction task. MMR achieves superior performance
compared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves
competitive performance with SOTA methods to detect the anomalies of different
types on the MVTec AD dataset. Code and dataset are available at
https://github.com/zhangzilongc/MMR.
|
[
{
"version": "v1",
"created": "Wed, 5 Apr 2023 04:07:54 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 07:26:08 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Zhang",
"Zilong",
""
],
[
"Zhao",
"Zhibin",
""
],
[
"Zhang",
"Xingwu",
""
],
[
"Sun",
"Chuang",
""
],
[
"Chen",
"Xuefeng",
""
]
] |
new_dataset
| 0.999571 |
2304.03763
|
Fangyin Wei
|
Fangyin Wei, Thomas Funkhouser, Szymon Rusinkiewicz
|
Clutter Detection and Removal in 3D Scenes with View-Consistent
Inpainting
|
18 pages. ICCV 2023. Project page:
https://weify627.github.io/clutter/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Removing clutter from scenes is essential in many applications, ranging from
privacy-concerned content filtering to data augmentation. In this work, we
present an automatic system that removes clutter from 3D scenes and inpaints
with coherent geometry and texture. We propose techniques for its two key
components: 3D segmentation from shared properties and 3D inpainting, both of
which are important problems. The definition of 3D scene clutter
(frequently-moving objects) is not well captured by commonly-studied object
categories in computer vision. To tackle the lack of well-defined clutter
annotations, we group noisy fine-grained labels, leverage virtual rendering,
and impose an instance-level area-sensitive loss. Once clutter is removed, we
inpaint geometry and texture in the resulting holes by merging inpainted RGB-D
images. This requires novel voting and pruning strategies that guarantee
multi-view consistency across individually inpainted images for mesh
reconstruction. Experiments on ScanNet and Matterport dataset show that our
method outperforms baselines for clutter segmentation and 3D inpainting, both
visually and quantitatively.
|
[
{
"version": "v1",
"created": "Fri, 7 Apr 2023 17:57:20 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 15:22:19 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Wei",
"Fangyin",
""
],
[
"Funkhouser",
"Thomas",
""
],
[
"Rusinkiewicz",
"Szymon",
""
]
] |
new_dataset
| 0.999333 |
2304.11496
|
Shathushan Sivashangaran
|
Shathushan Sivashangaran, Apoorva Khairnar and Azim Eskandarian
|
AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for
Sim-to-Real Deep Reinforcement Learning
|
$\copyright$ 2023 the authors. This work has been accepted to IFAC
for publication under a Creative Commons License CC-BY-NC-ND
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle
(AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which
is a necessity in hazardous, information poor environments such as regions
where natural disasters occur, and extraterrestrial planets. The substantial
training time required to learn an optimal DRL policy, which can be days or
weeks for complex tasks, is a major hurdle to real-world implementation in AGV
applications. Training entails repeated collisions with the surrounding
environment over an extended time period, dependent on the complexity of the
task, to reinforce positive exploratory, application specific behavior that is
expensive, and time consuming in the real-world. Effectively bridging the
simulation to real-world gap is a requisite for successful implementation of
DRL in complex AGV applications, enabling learning of cost-effective policies.
We present AutoVRL, an open-source high fidelity simulator built upon the
Bullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch to
train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with
sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV
control, and realistic environments, with extensibility for new environments
and AGV models. The simulator provides access to state-of-the-art DRL
algorithms, utilizing a python interface for simple algorithm and environment
customization, and simulation execution.
|
[
{
"version": "v1",
"created": "Sat, 22 Apr 2023 23:14:56 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 04:35:06 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Sivashangaran",
"Shathushan",
""
],
[
"Khairnar",
"Apoorva",
""
],
[
"Eskandarian",
"Azim",
""
]
] |
new_dataset
| 0.990908 |
2305.07270
|
Kailun Yang
|
Xuan He, Fan Yang, Kailun Yang, Jiacheng Lin, Haolong Fu, Meng Wang,
Jin Yuan, Zhiyong Li
|
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for
Monocular 3D Object Detection
|
Accepted to IEEE Transactions on Intelligent Vehicles (T-IV). Code
will be made publicly available at
https://github.com/mikasa3lili/SSD-MonoDETR
| null | null | null |
cs.CV cs.RO eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transformer-based methods have demonstrated superior performance for
monocular 3D object detection recently, which aims at predicting 3D attributes
from a single 2D image. Most existing transformer-based methods leverage both
visual and depth representations to explore valuable query points on objects,
and the quality of the learned query points has a great impact on detection
accuracy. Unfortunately, existing unsupervised attention mechanisms in
transformers are prone to generate low-quality query features due to inaccurate
receptive fields, especially on hard objects. To tackle this problem, this
paper proposes a novel "Supervised Scale-aware Deformable Attention" (SSDA) for
monocular 3D object detection. Specifically, SSDA presets several masks with
different scales and utilizes depth and visual features to adaptively learn a
scale-aware filter for object query augmentation. Imposing the scale awareness,
SSDA could well predict the accurate receptive field of an object query to
support robust query feature generation. Aside from this, SSDA is assigned with
a Weighted Scale Matching (WSM) loss to supervise scale prediction, which
presents more confident results as compared to the unsupervised attention
mechanisms. Extensive experiments on the KITTI and Waymo Open datasets
demonstrate that SSDA significantly improves the detection accuracy, especially
on moderate and hard objects, yielding state-of-the-art performance as compared
to the existing approaches. Our code will be made publicly available at
https://github.com/mikasa3lili/SSD-MonoDETR.
|
[
{
"version": "v1",
"created": "Fri, 12 May 2023 06:17:57 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Jun 2023 05:26:17 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Jul 2023 05:18:56 GMT"
},
{
"version": "v4",
"created": "Fri, 1 Sep 2023 16:17:54 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"He",
"Xuan",
""
],
[
"Yang",
"Fan",
""
],
[
"Yang",
"Kailun",
""
],
[
"Lin",
"Jiacheng",
""
],
[
"Fu",
"Haolong",
""
],
[
"Wang",
"Meng",
""
],
[
"Yuan",
"Jin",
""
],
[
"Li",
"Zhiyong",
""
]
] |
new_dataset
| 0.958841 |
2305.16759
|
Takato Yoshikawa
|
Takato Yoshikawa, Yuki Endo, Yoshihiro Kanamori
|
StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human
| null | null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper tackles text-guided control of StyleGAN for editing garments in
full-body human images. Existing StyleGAN-based methods suffer from handling
the rich diversity of garments and body shapes and poses. We propose a
framework for text-guided full-body human image synthesis via an
attention-based latent code mapper, which enables more disentangled control of
StyleGAN than existing mappers. Our latent code mapper adopts an attention
mechanism that adaptively manipulates individual latent codes on different
StyleGAN layers under text guidance. In addition, we introduce feature-space
masking at inference time to avoid unwanted changes caused by text inputs. Our
quantitative and qualitative evaluations reveal that our method can control
generated images more faithfully to given texts than existing methods.
|
[
{
"version": "v1",
"created": "Fri, 26 May 2023 09:21:56 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Jul 2023 08:39:31 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Sep 2023 09:13:10 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Yoshikawa",
"Takato",
""
],
[
"Endo",
"Yuki",
""
],
[
"Kanamori",
"Yoshihiro",
""
]
] |
new_dataset
| 0.996354 |
2306.11300
|
Zilun Zhang
|
Zilun Zhang, Tiancheng Zhao, Yulong Guo, Jianwei Yin
|
RS5M: A Large Scale Vision-Language Dataset for Remote Sensing
Vision-Language Foundation Model
|
RS5M dataset v4
| null | null | null |
cs.CV cs.AI cs.CL cs.MM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Pre-trained Vision-Language Foundation Models utilizing extensive image-text
paired data have demonstrated unprecedented image-text association
capabilities, achieving remarkable results across various downstream tasks. A
critical challenge is how to make use of existing large-scale pre-trained VLMs,
which are trained on common objects, to perform the domain-specific transfer
for accomplishing domain-related downstream tasks. In this paper, we propose a
new framework that includes the Domain Foundation Model (DFM), bridging the gap
between the General Foundation Model (GFM) and domain-specific downstream
tasks. Moreover, we present an image-text paired dataset in the field of remote
sensing (RS), RS5M, which has 5 million RS images with English descriptions.
The dataset is obtained from filtering publicly available image-text paired
datasets and captioning label-only RS datasets with pre-trained VLM. These
constitute the first large-scale RS image-text paired dataset. Additionally, we
tried several Parameter-Efficient Fine-Tuning methods on RS5M to implement the
DFM. Experimental results show that our proposed dataset are highly effective
for various tasks, improving upon the baseline by $8 \% \sim 16 \%$ in
zero-shot classification tasks, and obtaining good results in both
Vision-Language Retrieval and Semantic Localization tasks.
\url{https://github.com/om-ai-lab/RS5M}
|
[
{
"version": "v1",
"created": "Tue, 20 Jun 2023 05:30:59 GMT"
},
{
"version": "v2",
"created": "Thu, 31 Aug 2023 22:33:54 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Zhang",
"Zilun",
""
],
[
"Zhao",
"Tiancheng",
""
],
[
"Guo",
"Yulong",
""
],
[
"Yin",
"Jianwei",
""
]
] |
new_dataset
| 0.999575 |
2306.11702
|
Chen Zui
|
Zui Chen, Lei Cao, Sam Madden
|
Lingua Manga: A Generic Large Language Model Centric System for Data
Curation
|
4 pages, 6 figures, VLDB 2023 Demo paper
| null | null | null |
cs.DB cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Data curation is a wide-ranging area which contains many critical but
time-consuming data processing tasks. However, the diversity of such tasks
makes it challenging to develop a general-purpose data curation system. To
address this issue, we present Lingua Manga, a user-friendly and versatile
system that utilizes pre-trained large language models. Lingua Manga offers
automatic optimization for achieving high performance and label efficiency
while facilitating flexible and rapid development. Through three example
applications with distinct objectives and users of varying levels of technical
proficiency, we demonstrate that Lingua Manga can effectively assist both
skilled programmers and low-code or even no-code users in addressing data
curation challenges.
|
[
{
"version": "v1",
"created": "Tue, 20 Jun 2023 17:30:02 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 15:40:40 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Chen",
"Zui",
""
],
[
"Cao",
"Lei",
""
],
[
"Madden",
"Sam",
""
]
] |
new_dataset
| 0.999569 |
2306.13177
|
Baolin Li
|
Baolin Li, Rohan Basu Roy, Daniel Wang, Siddharth Samsi, Vijay
Gadepally, Devesh Tiwari
|
Toward Sustainable HPC: Carbon Footprint Estimation and Environmental
Implications of HPC Systems
| null | null |
10.1145/3581784.3607035
| null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The rapid growth in demand for HPC systems has led to a rise in carbon
footprint, which requires urgent intervention. In this work, we present a
comprehensive analysis of the carbon footprint of high-performance computing
(HPC) systems, considering the carbon footprint during both the hardware
manufacturing and system operational stages. Our work employs HPC hardware
component carbon footprint modeling, regional carbon intensity analysis, and
experimental characterization of the system life cycle to highlight the
importance of quantifying the carbon footprint of HPC systems.
|
[
{
"version": "v1",
"created": "Thu, 22 Jun 2023 19:38:54 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Aug 2023 05:51:48 GMT"
},
{
"version": "v3",
"created": "Thu, 31 Aug 2023 22:17:06 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Li",
"Baolin",
""
],
[
"Roy",
"Rohan Basu",
""
],
[
"Wang",
"Daniel",
""
],
[
"Samsi",
"Siddharth",
""
],
[
"Gadepally",
"Vijay",
""
],
[
"Tiwari",
"Devesh",
""
]
] |
new_dataset
| 0.991085 |
2308.01525
|
Jiyoung Lee
|
Jiyoung Lee, Seungho Kim, Seunghyun Won, Joonseok Lee, Marzyeh
Ghassemi, James Thorne, Jaeseok Choi, O-Kil Kwon, Edward Choi
|
VisAlign: Dataset for Measuring the Degree of Alignment between AI and
Humans in Visual Perception
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
AI alignment refers to models acting towards human-intended goals,
preferences, or ethical principles. Given that most large-scale deep learning
models act as black boxes and cannot be manually controlled, analyzing the
similarity between models and humans can be a proxy measure for ensuring AI
safety. In this paper, we focus on the models' visual perception alignment with
humans, further referred to as AI-human visual alignment. Specifically, we
propose a new dataset for measuring AI-human visual alignment in terms of image
classification, a fundamental task in machine perception. In order to evaluate
AI-human visual alignment, a dataset should encompass samples with various
scenarios that may arise in the real world and have gold human perception
labels. Our dataset consists of three groups of samples, namely Must-Act (i.e.,
Must-Classify), Must-Abstain, and Uncertain, based on the quantity and clarity
of visual information in an image and further divided into eight categories.
All samples have a gold human perception label; even Uncertain (severely
blurry) sample labels were obtained via crowd-sourcing. The validity of our
dataset is verified by sampling theory, statistical theories related to survey
design, and experts in the related fields. Using our dataset, we analyze the
visual alignment and reliability of five popular visual perception models and
seven abstention methods. Our code and data is available at
\url{https://github.com/jiyounglee-0523/VisAlign}.
|
[
{
"version": "v1",
"created": "Thu, 3 Aug 2023 04:04:03 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Sep 2023 08:52:02 GMT"
}
] | 2023-09-04T00:00:00 |
[
[
"Lee",
"Jiyoung",
""
],
[
"Kim",
"Seungho",
""
],
[
"Won",
"Seunghyun",
""
],
[
"Lee",
"Joonseok",
""
],
[
"Ghassemi",
"Marzyeh",
""
],
[
"Thorne",
"James",
""
],
[
"Choi",
"Jaeseok",
""
],
[
"Kwon",
"O-Kil",
""
],
[
"Choi",
"Edward",
""
]
] |
new_dataset
| 0.999857 |
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