Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification

Abstract

Unsupervised domain adaptation person re-identification (UDA Re-ID) aims to leverage the pedestrian knowledge learned from labeled source domain to assist in learning the pedestrian knowledge in the unlabeled target domain. Most of existing investigations typically utilize single-class center clustering algorithms to group unlabeled target domain instances. Unfortunately, single-class center clustering algorithms tend to cluster pedestrian pictures from different identities into the same cluster, leading to inaccurate labels. Training with these noisy labels can undesirably deteriorate the accuracy of UDA Re-ID. Responding to the problem, we propose a multi-class center dynamic contrastive learning (MCC-DCL) for UDA Re-ID, which includes three main parts: multi-center clustering (MCC), dynamic pseudo-labeling (DPL), and dynamic contrastive learning (DCL). In order to reduce noisy labels generated during clustering, we introduce MCC method to generates reliable pseudo-labels for instances. Furthermore, to fully utilize the knowledge learned by the network during each iteration, we propose DPL method to optimizes the pseudo-labels of instances. Finally, for improving the discriminative performance of model and its tolerance to noisy labels, we propose DCL method that utilizes dynamic pseudo-labels and dynamic contrastive loss for supervised training. Comprehensive experiments and analyses demonstrate that MCC-DCL significantly outperforms existing approaches in UDA Re-ID tasks.