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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops"
12857 条 记 录,以下是4931-4940 订阅
排序:
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
STaR: Self-supervised Tracking and Reconstruction of Rigid O...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Yuan, Wentao Lv, Zhaoyang Schmidt, Tanner Lovegrove, Steven Univ Washington Seattle WA 98195 USA Facebook Real Labs Res Menlo Pk CA USA
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural... 详细信息
来源: 评论
Nearest Neighbor Matching for Deep Clustering
Nearest Neighbor Matching for Deep Clustering
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Dang, Zhiyuan Deng, Cheng Yang, Xu Wei, Kun Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China JD Tech Beijing 100176 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA JD Finance Amer Corp Mountain View CA 94043 USA
Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and globa... 详细信息
来源: 评论
Domain Adaptation with Auxiliary Target Domain-Oriented Classifier
Domain Adaptation with Auxiliary Target Domain-Oriented Clas...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liang, Jian Hu, Dapeng Feng, Jiashi Natl Univ Singapore NUS Singapore Singapore Sea AI Lab SAIL Singapore Singapore
Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous ... 详细信息
来源: 评论
Animating General Image with Large Visual Motion Model
Animating General Image with Large Visual Motion Model
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conference on computer vision and pattern recognition (CVPR)
作者: Dengsheng Chen Xiaoming Wei Xiaolin Wei Meituan Beijing China
We present the pioneering Large Visual Motion Model (LVMM), meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model, fortified with a wealth of prior knowledge extra... 详细信息
来源: 评论
Understanding the Robustness of Skeleton-based Action recognition under Adversarial Attack
Understanding the Robustness of Skeleton-based Action Recogn...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wang, He He, Feixiang Peng, Zhexi Shao, Tianjia Yang, Yong-Liang Zhou, Kun Hogg, David Univ Leeds Leeds W Yorkshire England Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Univ Bath Bath Avon England
Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art... 详细信息
来源: 评论
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Frequency-aware Discriminative Feature Learning Supervised b...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Li, Jiaming Xie, Hongtao Li, Jiahong Wang, Zhongyuan Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China Kuaishou Technol Beijing Peoples R China
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignora... 详细信息
来源: 评论
Learning a Non-blind Deblurring Network for Night Blurry Images
Learning a Non-blind Deblurring Network for Night Blurry Ima...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chen, Liang Zhang, Jiawei Pan, Jinshan Lin, Songnan Fang, Faming Ren, Jimmy S. East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China SenseTime Hong Kong Peoples R China
Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we pro... 详细信息
来源: 评论
Multi-Objective Interpolation Training for Robustness to Label Noise
Multi-Objective Interpolation Training for Robustness to Lab...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ortego, Diego Arazo, Eric Albert, Paul O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Insight Ctr Data Analyt Dublin Ireland
Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robust classification loss functions. Con... 详细信息
来源: 评论
Background Splitting: Finding Rare Classes in a Sea of Background
Background Splitting: Finding Rare Classes in a Sea of Backg...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Mullapudi, Ravi Teja Poms, Fait Mark, William R. Ramanan, Deva Fatahalian, Kayvon Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Res Mountain View CA 94043 USA
We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dat... 详细信息
来源: 评论
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, CVPRW 2023
作者: Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. ... 详细信息
来源: 评论