Part-aware Attention Network for Person Re-Identification

Wangmeng Xiang, Jianqiang Huang, Xian-Sheng Hua, Lei Zhang; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020


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.

Related Material

@InProceedings{Xiang_2020_ACCV, author = {Xiang, Wangmeng and Huang, Jianqiang and Hua, Xian-Sheng and Zhang, Lei}, title = {Part-aware Attention Network for Person Re-Identification}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} }