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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"
3320 条 记 录,以下是201-210 订阅
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Cross-domain fashion image retrieval  31
Cross-domain fashion image retrieval
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Gajic, Bojana Baldrich, Ramon Univ Autonoma Barcelona Comp Vis Ctr Edifici O UAB Bellaterra Spain
Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image o... 详细信息
来源: 评论
Towards efficient feature sharing in MIMO architectures
Towards efficient feature sharing in MIMO architectures
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Sun, Remy Rame, Alexandre Masson, Clement Thome, Nicolas Cord, Matthieu Sorbonne Univ MLIA ISIR Paris France Conservatoire Natl Arts & Metiers CEDRIC Vertigo Paris France Thales Land & Air Syst Elancourt France Valeo Ai Paris France
Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, thes... 详细信息
来源: 评论
Adversarial Robust Model Compression using In-Train Pruning
Adversarial Robust Model Compression using In-Train Pruning
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Vemparala, Manoj-Rohit Fasfous, Nael Frickenstein, Alexander Sarkar, Sreetama Zhao, Qi Kuhn, Sabine Frickenstein, Lukas Singh, Anmol Unger, Christian Nagaraja, Naveen-Shankar Wressnegger, Christian Stechele, Walter BMW Autonomous Driving Munich Germany Tech Univ Munich Munich Germany Karlsruhe Inst Technol Karlsruhe Germany
Efficiently deploying learning-based systems on embedded hardware is challenging for various reasons, two of which are considered in this paper: The model's size and its robustness against attacks. Both need to be... 详细信息
来源: 评论
PAND: Precise Action recognition on Naturalistic Driving
PAND: Precise Action Recognition on Naturalistic Driving
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zhao, Hangyue Xiao, Yuchao Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China
Temporal action localization for untrimmed videos is a difficult problem in computer vision. It is challenge to infer the start and end of activity instances on small-scale datasets covering multi-view information acc... 详细信息
来源: 评论
A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees
A Vision-based System for Traffic Anomaly Detection using De...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA
Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting ano... 详细信息
来源: 评论
Domain Adaptable Normalization for Semi-Supervised Action recognition in the Dark
Domain Adaptable Normalization for Semi-Supervised Action Re...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liang, Zixi Chen, Jiajun Chen, Rui Zheng, Bingbing Zhou, Mingyue Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co Guangzhou Peoples R China
Action recognition in the dark is gaining more and more attention with the rapid development of intelligent recognition applications in real-world applications, e.g. self-driving at night and night surveillance. Howev... 详细信息
来源: 评论
Dual-Branch Collaborative Transformer for Virtual Try-On
Dual-Branch Collaborative Transformer for Virtual Try-On
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Fenocchi, Emanuele Morelli, Davide Cornia, Marcella Baraldi, Lorenzo Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET PORTER GRP Milan Italy
Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional ... 详细信息
来源: 评论
Anticipation of Human Actions with Pose-based Fine-grained Representations  32
Anticipation of Human Actions with Pose-based Fine-grained R...
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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Agethen, Sebastian Lee, Hu-Cheng Hsu, Winston H. Natl Taiwan Univ Taipei Taiwan
Anticipating an action that is about to happen allows us to be more efficient in interacting with our environment. However, prediction is a challenging task in computer vision, because videos are only partially availa... 详细信息
来源: 评论
Learning Unbiased Representations via Mutual Information Backpropagation
Learning Unbiased Representations via Mutual Information Bac...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ragonesi, Ruggero Volpi, Riccardo Cavazza, Jacopo Murino, Vittorio Ist Italiano Tecnol PAVIS Dept Genoa Italy Univ Genoa DITEN Dept Genoa Italy Naver Labs Europe Grenoble France Univ Verona Dept Comp Sci Verona Italy Huawei Technol Ltd Ireland Res Ctr Dublin Ireland
We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by ... 详细信息
来源: 评论
Dataset Distillation by Matching Training Trajectories
Dataset Distillation by Matching Training Trajectories
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA
Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new fo... 详细信息
来源: 评论