30/11/2020

Part-aware Attention Network for Person Re-Identification

Wangmeng Xiang, Jianqiang Huang, Xian-Sheng Hua, Lei Zhang

Keywords:

Abstract Paper Similar Papers

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.

The video of this talk cannot be embedded. You can watch it here:
https://accv2020.github.io/miniconf/poster_275.html
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at ACCV 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 3:01 
 0
 0
 0
 0
 7:58 
 0
 0
 0
 0
 3:06 
 0
 0
 0
 0
 3:01 
 0
 0
 0
 0
 14:11 
 0
 0
 0
 0
 18:54 
 0
 0
 0
 0
 5:02 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 14:04 
 0
 0
 0
 0
 2:51 
 0
 0
 0
 0
 13:59 
 0
 0
 0
 0
 6:16 
 0
 0
 0
 0
 13:20 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 15:02 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 14:40 
 0
 0
 0
 0
 15:15 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 14:45 
 0
 0
 0
 0
 2:57 
 0
 0
 0
 0
 13:16 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 2:53 
 0
 0
 0
 0
 9:24 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:03 
 0
 0
 0
 0
 14:26 
 0
 0
 0
 0
 8:53 
 0
 0
 0
 0
 1:03 
 0
 0
 0
 0
 18:32 
 0
 0
 0
 0
 3:03 
 0
 0
 0
 0
 4:53 
 0
 0
 0
 0
 3:18