A negative transfer approach to person re-identification via domain augmentation

Abstract Despite recent remarkable progress, person re-identification (ReID) still suffers from a shortage of annotated training data. To deal with this problem, there has been a boost of interest in developing various data augmentation methods. In this paper, we are devoted to developing an end-to-end joint representation learning framework for the ReID task on the basis of a novel data augmentation strategy. Specifically, we regard the original training dataset as a source domain and generate the counterpart augmented domains through image channel shuffling. Accordingly, we design a symmetric classification network for ReID learning. By investigating the domain-level and identity-level relationship between domains, we use the idea of negative transfer and structural consistency to optimize the network for learning discriminative feature embeddings. Comprehensive experiments on some benchmark datasets demonstrate the effectiveness and robustness of our proposed approach. Source code is provided for reproducibility and will be released at: https://github.com/flychen321/negative_transfer_reid.