Weak Reverse attention with Context Aware for Person Re-identification

Abstract

Person re-identification is a difficult topic in computer vision. Some study think that current deep learning methods is biased to capture the most discriminative features and ignore low-level details, more serious is it pay too much attention on relevance between background appearances of person images. It might limit their accuracy or makes them needlessly expensive for a not best performance. In this paper, we carefully design the Weak Reverse attention with Context Aware Network (WRCANet). Specifically, by merging weak reverse attention network and content aware module, the model can not only remove the background noise to extract the main information of persons, but also suppress the loss of local detailed information as the network deepens. We experiment on the Market-1501, DukeMTMC-reID and CUHK03, and the results show that our method achieves the state-of-the-art performance.