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Part-Aware Attention Network for Person Re-identification

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Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12625))

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Abstract

Multi-level feature aggregation and part feature extraction are widely used to boost the performance of person re-identification (Re-ID). Most multi-level feature aggregation methods treat feature maps on different levels equally and use simple local operations for feature fusion, which neglects the long-distance connection among feature maps. On the other hand, the popular horizon pooling part based feature extraction methods may lead to feature misalignment. In this paper, we propose a novel Part-aware Attention Network (PAN) to connect part feature maps and middle-level features. Given a part feature map and a source feature map, PAN uses part features as queries to perform second-order information propagation from the source feature map. The attention is computed based on the compatibility of the source feature map with the part feature map. Specifically, PAN uses high-level part features of different human body parts to aggregate information from mid-level feature maps. As a part-aware feature aggregation method, PAN operates on all spatial positions of feature maps so that it can discover long-distance relations. Extensive experiments show that PAN achieves leading performance on Re-ID benchmarks Market1501, DukeMTMC, and CUHK03.

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Acknowledgements

This research is supported by the China NSFC grant (no. 61672446).

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Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Wangmeng Xiang & Lei Zhang

  2. Artificial Intelligence Center, Alibaba DAMO Academy, Hangzhou, China

    Jianqiang Huang, Xian-Sheng Hua & Lei Zhang

Authors
  1. Wangmeng Xiang
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  2. Jianqiang Huang
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  3. Xian-Sheng Hua
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  4. Lei Zhang
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Corresponding author

Correspondence to Lei Zhang .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Xiang, W., Huang, J., Hua, XS., Zhang, L. (2021). Part-Aware Attention Network for Person Re-identification. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-69538-5_9

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  • Online ISBN: 978-3-030-69538-5

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