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SANet: Statistic Attention Network for Video-Based Person Re-Identification.

Authors :
Bai, Shutao
Ma, Bingpeng
Chang, Hong
Huang, Rui
Shan, Shiguang
Chen, Xilin
Source :
IEEE Transactions on Circuits & Systems for Video Technology; Jun2022, Vol. 32 Issue 6, p3866-3879, 14p
Publication Year :
2022

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10518215
Volume :
32
Issue :
6
Database :
Complementary Index
Journal :
IEEE Transactions on Circuits & Systems for Video Technology
Publication Type :
Academic Journal
Accession number :
157258498
Full Text :
https://doi.org/10.1109/TCSVT.2021.3119983