Hierarchical Proxy Learning for Cloth-Changing Person Re-Identification

Cloth-Changing person Re-Identification (CC-ReID) depends significantly on learning discriminative features under the cloth-changing scenario. It is quite challenging due to the large intra-person variance and small inter-person variance caused by clothes changing. To address these issues, in this work we propose a Hierarchical Proxy Learning (HPL) framework to extract clothes-irrelevant and person-invariant features. Specifically, we employ person labels as the main proxy. Instead of leveraging clothing labels as sub proxy, we further propose a clustering-based automatic sub-proxy mining scheme. More specifically, we first construct a person-aware Main Proxy Learning (MPL) to improve the separability of different persons. Then, a Sub Proxy Learning (SPL) is constructed to enhance the intra-person compactness. Finally, a Sub-to-Main Proxy Learning (S2MPL) is proposed to promote the cooperation between the main proxies and sub proxies. In addition, to weed out the negative effect of clothes, we propose a Sample Balance and Diversity (SBD) module, which balances the number of sub proxies in a mini-batch and utilizes semantic guidance to enrich the diversity of clothes, simultaneously. Extensive experiments on two public CC-ReID datasets demonstrate the superiority of our proposed method over most state-of-the-art methods.