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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是181-190 订阅

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

引用

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

来源：评论

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Bridging the Gap Between Automated and Human Facial Emotion Perception

Bridging the Gap Between Automated and Human Facial Emotion ...

引用

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

作者： Stratton, Derek Hand, Emily Univ Nevada Reno Reno NV 89557 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the complex relationship between emotions and facial expressions is important for both psychologists and computer scientists. A large body of research in psychology investigates facial expressions, emotions, and how emotions are perceived from facial expressions. As computer scientists look to incorporate this research into automatic emotion perception systems, it is important to understand the nature and limitations of human emotion perception. These principles of emotion science affect the way datasets are created, methods are implemented, and results are interpreted in automated emotion perception. This paper aims to distill and align prior work in automated and human facial emotion perception to facilitate future discussions and research at the intersection of the two disciplines.

关键词： Emotion recognition computer vision Uncertainty conferences Sociology Psychology Natural language processing

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ConCon-Chi: Concept-Context Chimera Benchmark for Personalized vision-Language Tasks

ConCon-Chi: Concept-Context Chimera Benchmark for Personaliz...

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2024 ieee/CVF conference on computer vision and pattern recognition, cvpr 2024

作者： Rosasco, Andrea Berti, Stefano Pasquale, Giulia Malafronte, Damiano Sato, Shogo Segawa, Hiroyuki Inada, Tetsugo Natale, Lorenzo University of Genoa Italy Istituto Italiano di Tecnologia Italy Sony Interactive Entertainment Inc. Japan

ISBN: (纸本)9798350353006

While recent vision-Language (VL) models excel at open-vocabulary tasks, it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge, where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG, designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large, varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts, we combined different contexts with the same concept, and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties, and we propose principles and a dataset for their performance assessment. Dataset: https://***/10.48557/QJ1166 and code: https://***/hsp-iit/concon-chi-benchmark. © 2024 ieee.

关键词： benchmark chimeric objects compositionality dataset personalization personalized text-to-image generation personalized text-to-image retrieval personalized vision-language models personalized vision-language tasks vision-language models vision-language tasks

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Importance is in your attention: agent importance prediction for autonomous driving

Importance is in your attention: agent importance prediction...

引用

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

作者： Hazard, Christopher Bhagat, Akshay Buddharaju, Balarama Raju Liu, Zhongtao Shao, Yunming Lu, Lu Omari, Sammy Cui, Henggang Motional Boston MA 02210 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory prediction models often use attention mechanisms to model the interaction between agents. In this paper, we show that the attention information from such models can also be used to measure the importance of each agent with respect to the ego vehicle's future planned trajectory. Our experiment results on the nuPlans dataset show that our method can effectively find and rank surrounding agents by their impact on the ego's plan.

关键词： computer vision conferences Predictive models Trajectory pattern recognition Task analysis Autonomous vehicles

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Isolated Sign Language recognition based on Tree Structure Skeleton Images

Isolated Sign Language Recognition based on Tree Structure S...

引用

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

作者： Laines, David Gonzalez-Mendoza, Miguel Ochoa-Ruiz, Gilberto Bejarano, Gissella Tecnol Monterrey Sch Sci & Engn Eugenio Tecnol Ave Eugenio Garza Sada 2501 Sur Monterrey 64849 NL Mexico Univ Peruana Cayetano Heredia Urb Ingn Ave Honorio Delgado 430 Lima Peru

ISBN: (纸本)9798350302493

Sign Language recognition (SLR) systems aim to be embedded in video stream platforms to recognize the sign performed in front of a camera. SLR research has taken advantage of recent advances in pose estimation models to use skeleton sequences estimated from videos instead of RGB information to predict signs. This approach can make HAR-related tasks less complex and more robust to diverse backgrounds, lightning conditions, and physical appearances. In this work, we explore the use of a spatio-temporal skeleton representation such as Tree Structure Skeleton Image (TSSI) as an alternative input to improve the accuracy of skeleton-based models for SLR. TSSI converts a skeleton sequence into an RGB image where the columns represent the joints of the skeleton in a depth-first tree traversal order, the rows represent the temporal evolution of the joints, and the three channels represent the (x, y, z) coordinates of the joints. We trained a DenseNet-121 using this type of input and compared it with other skeleton-based deep learning methods using a large-scale American Sign Language (ASL) dataset, WLASL. Our model (SL-TSSI-DenseNet) overcomes the state-of-the-art of other skeleton-based models. Moreover, when including data augmentation our proposal achieves better results than both skeleton-based and RGB-based models. We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset. On the AUTSL dataset, the model achieves similar results to the state-of-the-art of other skeleton-based models. On the LSM dataset, the model achieves higher results than the baseline. As far as we know, our work is the first to try TSSI for sign language recognition and our results suggest it presents a real alternative for isolated sign language representation. Code has been made available at: https://***/davidlainesv/SL-TSSI-DenseNet.

关键词： Image enhancement

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Persistent-Transient Duality in Human Behavior Modeling

Persistent-Transient Duality in Human Behavior Modeling

引用

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

作者： Hung Tran Vuong Le Venkatesh, Svetha Truyen Tran Deakin Univ Appl AI Inst Geelong Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions. The short-lived transient sessions are managed by a proposed Transient Switch. The neural framework is trained to discover the structure of the duality automatically. Our model shows superior performances in human-object interaction motion prediction.

关键词： computer vision Computational modeling conferences Dynamics Neural networks Switches Predictive models

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Proceedings - 2023 ieee/CVF conference on computer vision and pattern recognition, cvpr 2023

Proceedings - 2023 IEEE/CVF Conference on Computer Vision an...

引用

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

ISBN: (纸本)9798350301298

The proceedings contain 2356 papers. The topics discussed include: exploring discontinuity for video frame interpolation;two-view geometry scoring without correspondences;language-guided audio-visual source separation via trimodal consistency;handwritten text generation from visual archetypes;Bayesian posterior approximation with stochastic ensembles;ERM-KTP: knowledge-level machine unlearning via knowledge transfer;PlenVDB: memory efficient VDB-based radiance fields for fast training and rendering;learning and aggregating lane graphs for urban automated driving;teaching matters: investigating the role of supervision in vision transformers;NeuralField-LDM: scene generation with hierarchical latent diffusion models;cut and learn for unsupervised object detection and instance segmentation;probabilistic debiasing of scene graphs;and unifying layout generation with a decoupled diffusion model.

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

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

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

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Live Demo: E2P-Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P-Events to Polarization Reconstruction from PD...

引用

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

作者： Delbruck, Tobi Wang, Zuowen Mei, Haiyang Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Gruev, Viktor Univ. of Zurich and ETH Zurich Sensors Group Institute of Neuroinformatics Switzerland Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria Western Sydney University Intl. Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Dept. of Electrical and Computer Engineering UrbanaIL United States

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main cvpr conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [... 详细信息

ISBN: (纸本)9798350302493

关键词： Polarization

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Robustness and Adaptation to Hidden Factors of Variation

Robustness and Adaptation to Hidden Factors of Variation

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

作者： Paul, William Burlina, Philippe Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data. Towards this end, we employ a two step strategy that a) does unsupervised discovery, via generative models, of sensitive factors that cause models to under-perform, and b) intervenes models to make their performance invariant to these sensitive factors' influence. We consider 3 separate interventions for robustness, including: data augmentation, semantic consistency, and adversarial alignment. We evaluate our method using metrics that measure trade offs between invariance (insensitivity) and overall performance (utility) and show the benefits of our method for 3 settings (unsupervised, semi-supervised and generalization).

关键词： Measurement computer vision conferences Semantics Robustness Data models pattern recognition

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