Cascade Attention Network For Person Re-Identification

Person re-identification is a challenging task due to the view-point, illumination and pose variations. Recent works focus on extracting part-level features to offer beneficial fine-grained information. However, the part misalignment as well as the multi-stage training process limits their performance. Inspired by the human visual attention mechanism, this paper builds a cascade attention network(CAN) to learn the discriminative person features in a coarse-to-fine manner. Firstly, we employ the human semantic parsing module to generate coarse-grained part-level attention, which corresponds to the division of human body parts and can effectively filter the background noise. Then, to extract the local detailed features within each part, we introduce spatial-channel attention module to generate fine-grained pixel-level attention, which can further highlight the distinctive characteristics and repress the irrelevant ones. Finally, we can obtain an efficient person feature descriptor by combining both the global and local features. The whole learning process is conducted end-to-end. Experimental results show that the proposed method not only considerably outperforms its counter part but also achieves competitive performance on Market-1501 and DukeMTMC.