Person Re-Identification via Dual-Path Attention

In order to solve the problem of insufficient attention to non-salient distinguishable features and insufficient expression of extracted key features of pedestrians in current Re-ID methods, a feature extraction network based on a dual-path attention mechanism is proposed (Dual-Path Attention Network), which consists of a dual-path attention backbone network. It is composed of an enhanced attention feature fusion module. Among them, the dual-path attention network allows the model to focus on effective feature areas with different salience levels, which can be used to mine significant and potentially non-salient distinguishable features respectively, emphasizing the importance of potential key features.The enhanced attention feature fusion module is used to complete feature information complementation, and counterfactual intervention is used to enhance the quality and effectiveness of the learned attention feature map, so as to make the final feature representation more discriminative. Extensive experiments were conducted on Market1501, DukeMTMC-reID and MSMT17 data sets, and the results showed that the mAP values reached 89.3%, 80.0%, 58.4% respectively; the Rank-1 values reached 95.7%, 89.8%, 80.7% respectively, which fully proved the superiority of this method.