SANet: Statistic Attention Network for Video-Based Person Re-Identification
文献类型：期刊论文
| 作者 | Bai, Shutao3,4; Ma, Bingpeng3; Chang, Hong3,4; Huang, Rui1,2; Shan, Shiguang3,4; Chen, Xilin3,4 |
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 出版日期 | 2022-06-01 |
| 卷号 | 32期号:6页码:3866-3879 |
| 关键词 | Feature extraction Task analysis Computational modeling Visualization Video sequences Fuses Computer science Person re-identification self-attention long-range dependencies high-order statistics |
| ISSN号 | 1051-8215 |
| DOI | 10.1109/TCSVT.2021.3119983 |
| 英文摘要 | Capturing long-range dependencies during feature extraction is crucial for video-based person re-identification (re-id) since it would help to tackle many challenging problems such as occlusion and dramatic pose variation. Moreover, capturing subtle differences, such as bags and glasses, is indispensable to distinguish similar pedestrians. In this paper, we propose a novel and efficacious Statistic Attention (SA) block which can capture both the long-range dependencies and subtle differences. SA block leverages high-order statistics of feature maps, which contain both long-range and high-order information. By modeling relations with these statistics, SA block can explicitly capture long-range dependencies with less time complexity. In addition, high-order statistics usually concentrate on details of feature maps and can perceive the subtle differences between pedestrians. In this way, SA block is capable of discriminating pedestrians with subtle differences. Furthermore, this lightweight block can be conveniently inserted into existing deep neural networks at any depth to form Statistic Attention Network (SANet). To evaluate its performance, we conduct extensive experiments on two challenging video re-id datasets, showing that our SANet outperforms the state-of-the-art methods. Furthermore, to show the generalizability of SANet, we evaluate it on three image re-id datasets and two more general image classification datasets, including ImageNet. The source code is available at http://vipl.ict.ac.cn/resources/codes/code/SANet_code.zip. |
| 资助项目 | National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China (NSFC)[61876171] ; Natural Science Foundation of China (NSFC)[61976203] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000805833400046 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19604]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Ma, Bingpeng |
| 作者单位 | 1.Shenzhen Inst Artificial Intelligence & Robot, Shenzhen 518172, Guangdong, Peoples R China 2.Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Guangdong, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bai, Shutao,Ma, Bingpeng,Chang, Hong,et al. SANet: Statistic Attention Network for Video-Based Person Re-Identification[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(6):3866-3879. |
| APA | Bai, Shutao,Ma, Bingpeng,Chang, Hong,Huang, Rui,Shan, Shiguang,&Chen, Xilin.(2022).SANet: Statistic Attention Network for Video-Based Person Re-Identification.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(6),3866-3879. |
| MLA | Bai, Shutao,et al."SANet: Statistic Attention Network for Video-Based Person Re-Identification".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.6(2022):3866-3879. |
入库方式： OAI收割
来源：计算技术研究所
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