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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是1001-1010 订阅

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Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing

Supervised Contrastive Learning for Snapshot Spectral Imagin...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chuanbiao Song Yan Hong Jun Lan Huijia Zhu Weiqiang Wang Jianfu Zhang Ant Group Qing Yuan Research Institute Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study reveals a cutting-edge re-balanced contrastive learning strategy aimed at strengthening face anti-spoofing capabilities within facial recognition systems, with a focus on countering the challenges posed by printed photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS dataset, which benefits from Snapshot Spectral Imaging technology to provide hyperspectral images, our approach harmonizes class-level contrastive learning with data resampling and an innovative real-face oriented reweighting technique. This method effectively mitigates dataset imbalances and reduces identity-related biases. Notably, our strategy achieved an unprecedented 0.0000% Average Classification Error Rate (ACER) on the HySpeFAS dataset, ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing Challenge on CVPR 2024.

关键词： Training computer vision Error analysis Face recognition conferences Neural networks Imaging

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Spot the GEO Satellites: From Dataset to Kelvins SpotGEO Challenge

Spot the GEO Satellites: From Dataset to Kelvins SpotGEO Cha...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Bo Liu, Daqi Chin, Tat-Jun Rutten, Mark Derksenv, Dawa Martens, Marcus von Looz, Moritz Lecuyer, Gurvan Izzo, Dario Univ Adelaide Adelaide SA Australia InTrack Solut Adelaide SA Australia European Space Agcy Paris France

ISBN: (纸本)9781665448994

The Geosynchronous Equatorial Orbit (GEO) is home to many important space assets such as telecommunication and navigational satellites. Monitoring Resident Space Objects (RSOs) in GEO is a crucial aspect in achieving Space Situational Awareness (SSA) and in protecting critical space assets. However, ground-based GEO object detection is challenging due to the extreme distance of the targets, as well as nuisance factors including cloud coverage, atmospheric/weather effects, light pollution, sensor noise/defects, and star occlusions. The Kelvins SpotGEO Challenge is designed to establish to what extent images coming from a low-cost ground-based telescope can be used to detect GEO and near-GEO RSOs solely from photometric signals that are without any additional meta-data. At the same time, the SpotGEO dataset also addresses the lack of publicly available datasets from a computer vision perspective on the satellite detection problem;by assembling and releasing such a dataset, we hope to spur more efforts on the optical detection of RSOs and enable objective bench-marking for existing and future methods. In this work, we present details of the SpotGEO dataset development, challenge design, evaluation metric, and result analysis.

关键词： Space vehicles Measurement computer vision Satellites Pollution Kelvin Tools

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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Seongjae Min Junseok Yang Sejoon Lim Kookmin University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Deep learning Emotion recognition Gold computer vision Computational modeling Estimation Transformer cores

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From Implemented to Expected Behaviors: Leveraging Regression Oracles for Non-regression Fault Detection using LLMs 18

From Implemented to Expected Behaviors: Leveraging Regressio...

引用

18th ieee International conference on Software Testing, Verification and Validation workshops, ICSTW 2025

作者： Ruberto, Stefano Perera, Judith Jahangirova, Gunel Terragni, Valerio JRC European Commission Ispra Italy University of Auckland Auckland New Zealand King's College London London United Kingdom

ISBN: (纸本)9798331534677

Automated test generation tools often produce assertions that reflect implemented behavior, limiting their usage to regression testing. In this paper, we propose LLMProphet, a black-box approach that applies Few-Shot Learning with LLMs, using automatically generated regression tests as context to identify non-regression faults without relying on source code. By employing iterative cross-validation and a leave-one-out strategy, LLMProphet identifies regression assertions that are misaligned with expected behaviors. We outline LLMProphet's workflow, feasibility, and preliminary findings, demonstrating its potential for LLM-driven fault detection. © 2025 ieee.

关键词： Automatic test pattern generation

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Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML

Performance and Power: Systematic Evaluation of AI Workloads...

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2024 workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC workshops 2024

作者： John, Chelsea Maria Nassyr, Stepan Penke, Carolin Herten, Andreas Forschungszentrum Jülich Jülich Supercomputing Centre Jülich Germany

ISBN: (纸本)9798350355543

The rapid advancement of machine learning (ML) technologies has driven the development of specialized hardware accelerators designed to facilitate more efficient model training. This paper introduces the CARAML benchmark suite, which is employed to assess performance and energy consumption during the training of transformer-based large language models and computer vision models on a range of hardware accelerators, including systems from NVIDIA, AMD, and Graphcore. CARAML provides a compact, automated, extensible, and reproducible framework for assessing the performance and energy of ML workloads across various novel hardware architectures. The design and implementation of CARAML, along with a custom power measurement tool called jpwr, are discussed in detail. © 2024 ieee.

关键词： Accelerators AI Benchmark computer vision Energy GPU IPU Machine Learning NLP Performance Measurement

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One-shot action recognition in challenging therapy scenarios

One-shot action recognition in challenging therapy scenarios

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sabater, Alberto Santos, Laura Santos-Victor, Jose Bernardino, Alexandre Montesano, Luis Murillo, Ana C. Univ Lisbon Inst Super Tecn ISR Lisboa Lisbon Portugal Univ Zaragoza DIIS I3A Zaragoza Spain Bitbrain Technol Zaragoza Spain Politecn Milan Milan Italy

ISBN: (纸本)9781665448994

One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy.

关键词： Target recognition conferences Dynamics Medical treatment Kinematics pattern recognition Noise measurement

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EXCALIBUR: Encouraging and Evaluating Embodied Exploration

EXCALIBUR: Encouraging and Evaluating Embodied Exploration

引用

2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Zhu, Hao Kapoor, Raghav Min, So Yeon Han, Winson Li, Jiatai Geng, Kaiwen Neubig, Graham Bisk, Yonatan Kembhavi, Aniruddha Weihs, Luca Carnegie Mellon University United States Allen Institute for Artificial Intelligence

ISBN: (纸本)9798350301298

Experience precedes understanding. Humans constantly explore and learn about their environment out of curiosity, gather information, and update their models of the world. On the other hand, machines are either trained to learn passively from static and fixed datasets, or taught to complete specific goal-conditioned tasks. To encourage the development of exploratory interactive agents, we present the EXCALIBUR benchmark. EXCALIBUR allows agents to explore their environment for long durations and then query their understanding of the physical world via inquiries like: 'is the small heavy red bowl made from glass?' or 'is there a silver spoon heavier than the egg?'. This design encourages agents to perform free-form home exploration without myopia induced by goal conditioning. Once the agents have answered a series of questions, they can renter the scene to refine their knowledge, update their beliefs, and improve their performance on the questions. Our experiments demonstrate the challenges posed by this dataset for the present-day state-of-the-art embodied systems and the headroom afforded to develop new innovative methods. Finally, we present a virtual reality interface that enables humans to seamlessly interact within the simulated world and use it to gather human performance measures. EXCALIBUR affords unique challenges in comparison to presentday benchmarks and represents the next frontier for embodied AI research. © 2023 ieee.

关键词： Embodied vision: Active agents simulation

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On Accuracy and Speed of Geodesic Regression: Do Geometric Priors Improve Learning on Small Datasets?

On Accuracy and Speed of Geodesic Regression: Do Geometric P...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Adele Myers Nina Miolane UC Santa Barbara

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image datasets in specialized fields of science, such as biomedicine, are typically smaller than traditional machine learning datasets. As such, they present a problem for training many models. To address this challenge, researchers often attempt to incorporate priors, i.e., external knowledge, to help the learning procedure. Geometric priors, for example, offer to restrict the learning process to the manifold to which the data belong. However, learning on manifolds is sometimes computationally intensive to the point of being prohibitive. Here, we ask a provocative question: is machine learning on manifolds really more accurate than its linear counterpart to the extent that it is worth sacrificing significant speedup in computation? We answer this question through an extensive theoretical and experimental study of one of the most common learning methods for manifold-valued data: geodesic regression.

关键词： Manifolds Training Learning systems computer vision Accuracy conferences Computational modeling

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Discovering interpretable models of scientific image data with deep learning

Discovering interpretable models of scientific image data wi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Christopher J. Soelistyo Alan R. Lowe The Alan Turing Institute

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this study, we demonstrate the possibility of finding interpretable, domain-appropriate models of biological images, and propose that such a strategy can be used to derive scientific insight in domains involving raw data. This is achieved by the novel, concerted application of existing methods, namely, disentangled representation learning, sparse deep neural network training and symbolic regression. We demonstrate their relevance to the field of bioimaging using a well-studied test problem of classifying cell states in microscopy data. We find that such methods can produce highly parsimonious models that achieve ~ 98% of the accuracy of black-box benchmark models, with a tiny fraction of the complexity, and greater domain-appropriateness, as tested by adversarial attacks. As such, we provide proof of concept that interpretable, high-performing models can be used to produce scientific explanations of some underlying biological phenomenon.

关键词： Training Deep learning computer vision Biological system modeling Computational modeling Microscopy conferences

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Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks

Fast Walsh-Hadamard Transform and Smooth-Thresholding Based ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pan, Hongyi Badawi, Diaa Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60680 USA

ISBN: (纸本)9781665448994

In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace 1 x 1 convolution layers in deep neural networks. In the WHT domain, we denoise the transform domain coefficients using the new smooth-thresholding non-linearity, a smoothed version of the well-known soft-thresholding operator. We also introduce a family of multiplication-free operators from the basic 2x2 Hadamard transform to implement 3 x 3 depthwise separable convolution layers. Using these two types of layers, we replace the bottleneck layers in MobileNet-V2 to reduce the network's number of parameters with a slight loss in accuracy. For example, by replacing the final third bottleneck layers, we reduce the number of parameters from 2.270M to 947K. This reduces the accuracy from 95.21% to 92.88% on the CIFAR-10 dataset. Our approach significantly improves the speed of data processing. The fast Walsh-Hadamard transform has a computational complexity of O(mlog(2)m). As a result, it is computationally more efficient than the 1 x 1 convolution layer. The fast Walsh-Hadamard layer processes a tensor in R-10x32x32x1024 about 2 times faster than 1 x 1 convolution layer on NVIDIA Jetson Nano computer board.

关键词： Deep learning computer vision Tensors Convolution conferences Transforms computer architecture

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