Graph correlation-refined centroids for unsupervised person re-identification

This paper aims at studying unsupervised person re-identification (re-ID) which does not require any annotations. Recently, many approaches tackle this problem through contrastive learning due to its effective feature representation for unsupervised tasks. Especially, a uni-centroid representation is always obtained by averaging all the instance features within a cluster having the same pseudolabel. However, due to the unsatisfied clustering results, a cluster often contains some noisy samples, making the generated centroids imperfect. To address this issue, we propose a new graph correlation module (GCM) that can adaptively mine the relationship between each sample within the cluster and a high-quality relation-aware centroid is formed for momentum updating. Moreover, to increase the complexity of the task and prevent the model from falling into a local optimum, the original features extracted from the model are directly used to update the corresponding centroid. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art approaches on fully unsupervised re-ID tasks.