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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3161-3170 订阅
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Multi-Modal Learning with Missing Modality via Shared-Specific Feature Modelling
Multi-Modal Learning with Missing Modality via Shared-Specif...
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conference on computer vision and pattern recognition (CVPR)
作者: Hu Wang Yuanhong Chen Congbo Ma Jodie Avery Louise Hull Gustavo Carneiro The University of Adelaide Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey UK
The missing modality issue is critical but non-trivial to be solved by multi-modal models. Current methods aiming to handle the missing modality problem in multi-modal tasks, either deal with missing modalities only d...
来源: 评论
A Light Weight Model for Active Speaker Detection
A Light Weight Model for Active Speaker Detection
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conference on computer vision and pattern recognition (CVPR)
作者: Junhua Liao Haihan Duan Kanghui Feng Wanbing Zhao Yanbing Yang Liangyin Chen College of Computer Science Sichuan University Chengdu China The Chinese University of Hong Kong Shenzhen China The Institute for Industrial Internet Research Sichuan University Chengdu China
Active speaker detection is a challenging task in audiovisual scenarios, with the aim to detect who is speaking in one or more speaker scenarios. This task has received considerable attention because it is crucial in ...
来源: 评论
Rethinking Gradient Projection Continual Learning: Stability/Plasticity Feature Space Decoupling
Rethinking Gradient Projection Continual Learning: Stability...
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conference on computer vision and pattern recognition (CVPR)
作者: Zhen Zhao Zhizhong Zhang Xin Tan Jun Liu Yanyun Qu Yuan Xie Lizhuang Ma School of Computer Science and Technology East China Normal University Shanghai China Tencent Youtu Lab School of Informatics Xiamen University Fujian China
Continual learning aims to incrementally learn novel classes over time, while not forgetting the learned knowledge. Recent studies have found that learning would not forget if the updated gradient is orthogonal to the...
来源: 评论
Parametric Implicit Face Representation for Audio-Driven Facial Reenactment
Parametric Implicit Face Representation for Audio-Driven Fac...
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conference on computer vision and pattern recognition (CVPR)
作者: Ricong Huang Peiwen Lai Yipeng Qin Guanbin Li School of Computer Science and Engineering Sun Yat-sen University Cardiff University
Audio-driven facial reenactment is a crucial technique that has a range of applications in film-making, virtual avatars and video conferences. Existing works either employ explicit intermediate face representations (e...
来源: 评论
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
From Node Interaction to Hop Interaction: New Effective and ...
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conference on computer vision and pattern recognition (CVPR)
作者: Jie Chen Zilong Li Yin Zhu Junping Zhang Jian Pu Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms stil...
来源: 评论
Elastic Aggregation for Federated Optimization
Elastic Aggregation for Federated Optimization
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conference on computer vision and pattern recognition (CVPR)
作者: Dengsheng Chen Jie Hu Vince Junkai Tan Xiaoming Wei Enhua Wu Meituan State Key Laboratory of Computer Science ISCAS University of Chinese Academy of Sciences Bytedance Inc. University of Macau
Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients. FedAvg has become the preferred optimizer for federated learning because of its sim...
来源: 评论
Privacy-preserving Adversarial Facial Features
Privacy-preserving Adversarial Facial Features
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conference on computer vision and pattern recognition (CVPR)
作者: Zhibo Wang He Wang Shuaifan Jin Wenwen Zhang Jiahui Hut Yan Wang Peng Sun Wei Yuan Kaixin Liu Kui Rent School of Cyber Science and Technology Zhejiang University P. R. China ZJU-Hangzhou Global Scientific and Technological Innovation Center Alibaba Group P. R. China College of Computer Science and Electronic Engineering Hunan University P. R. China School of Cyber Science and Engineering Wuhan University P. R. China
Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for real-time recognition. However, such ...
来源: 评论
Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark
Ultra-High Resolution Segmentation with Ultra-Rich Context: ...
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conference on computer vision and pattern recognition (CVPR)
作者: Deyi Ji Feng Zhao Hongtao Lu Mingyuan Tao Jieping Ye University of Science and Technology of China Alibaba Group Department of Computer Science and Engineering Shanghai Jiao Tong University MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University
With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently ...
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Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation
Fuzzy Positive Learning for Semi-Supervised Semantic Segment...
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conference on computer vision and pattern recognition (CVPR)
作者: Pengchong Qiao Zhidan Wei Yu Wang Zhennan Wang Guoli Song Fan Xu Xiangyang Ji Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Automation and BNRist Tsinghua University Beijing China
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we t...
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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields
Clothed Human Performance Capture with a Double-layer Neural...
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conference on computer vision and pattern recognition (CVPR)
作者: Kangkan Wang Guofeng Zhang Suxu Cong Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China
This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover ...
来源: 评论