Adaptive weight part-based convolutional network for person re-identification

While part-based methods have been shown effective in the person re-identification task, it is unreasonable for most of them to treat each part equally, due to the retrieved image may be affected by deformation, occlusion and other factors, which makes the feature information of some parts unreliable. Instead of using the same weight of each part for the final person re-ID, we consider using an adaptive weight based on the part image information for each part for precise person retrieval. Specifically, we aim at learning discriminative part-informed features and propose an adaptive weight part-based convolutional network (AWPCN) for the person re-ID task. The core component of our AWPCN framework is an adaptive weight model, in which the part-based convolutional network and the adaptive weight model are used for feature refinement and feature-pair alignment, respectively. Given an image input at first, it outputs a convolutional descriptor consisting of several part-level features by the part-based convolutional network. And then, the corresponding weights of each part are determined by the adaptive weight model. Finally, we can use the adaptive weight part-based convolutional network joint to train each part loss and simultaneous optimization of its feature representations. We evaluate the proposed AWPCN model on Market-1501, DukeMTMC-reID and CUHK03 datasets. In extensive experiments, the AWPCN model outperforms most of the state-of-the-art methods on these representative datasets which clearly demonstrates the effectiveness of our proposed method. Our code will be released at https://github.com/deasonyuan/AWPCN.