Global Relation-Aware Contrast Learning for Unsupervised Person Re-Identification

Abstract

The goal of unsupervised person re-identification (Re-ID) is to use unlabeled person images to learn discriminative features. In recent years, many approaches have adopted clustered pseudo labels to construct proxies for contrastive learning, and have thereby achieved great success. However, existing methods of this kind only utilize local structures within IDs to design their proxies while ignoring the relations between samples of different IDs, which limits the improvement for inter-ID discriminative ability. To resolve this issue, we propose a Global Relation-Aware Contrast Learning (GRACL) method for the task of unsupervised Re-ID. Our method first sets up two proxies for each cluster to capture the inter- and intra-ID relations respectively, which enables us to both effectively increase inter-ID variances and reduce the intra-ID discrepancies. Specifically, the samples that are most different from those in different clusters are selected as inter-ID relation-aware proxies, while those that are least similar to samples from the same clusters are employed as intra-ID relation-aware proxies. With the aid of these proxies, we design both inter- and intra-ID relation-aware contrastive learning modules to facilitate model learning. By pulling each sample close to the positive proxy, we can obtain identity-invariant discriminative features. Experiments on five widely-used Re-ID datasets prove that our GRACL model outperforms current state-of-the-art approaches to a remarkable extent.