A Cloth-Irrelevant Harmonious Attention Network for Cloth-Changing Person Re-identification.

Cloth-changing person re-identification (CC-ReID) is a challenging task that aims at retrieving the target person across large spatial and temporal spans, with a high probability of changing clothes. To explicitly alleviate the impact of the person changing clothes on re-identification, this paper presents a cloth-irrelevant harmonious attention network (CIHANet) that learns cloth-irrelevant knowledge. Firstly, with the help of human parsing, the color information of human clothing is removed to generate black clothes images. Secondly, the raw person images are used to learn features with more color-based appearance knowledge, while the black clothes images are used to learn features with more cloth-irrelevant knowledge. Then, to fuse the knowledge of two distinct streams, we propose the harmonious attention module, including mutual learning attention and salience guided attention mechanisms. The mutual learning attention mechanism adaptively selects identity-relevant features across feature channels to make two streams interact with each other. The salience guided attention mechanism highlights the cloth-irrelevant areas by transferring the spatial knowledge from the black clothes stream to the raw images stream. Finally, quantitative and qualitative results on three CC-ReID datasets validate the superiority of our method on the CC-ReID task.