Part-aware attention network for person re-identification

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.