Boosting Person Re-Identification with Viewpoint Contrastive Learning and Adversarial Training

Person re-identification (ReID) aims at retrieving a person of interest across multiple cameras. Despite significant progress in person ReID, viewpoint variation remains an obstacle to extracting discriminative features for retrieval. To address this problem, we propose a Viewpoint-Robust Network (VRN) based on contrastive learning and adversarial training to boost person ReID. Specifically, a View-point Confusion (VC) module is proposed to conceal viewpoint information to extract viewpoint-agnostic features. We employ viewpoint contrastive learning to discriminate viewpoints, and then conversely ignore the viewpoint information by adversarial training. Besides, an ID Prototype (IDP) module further enhances the network by introducing a confidence-weighted IDP as a viewpoint-robust ID representation and conducting contrastive metric learning with an IDP triplet loss. Extensive experiments demonstrate the proposed method achieves state-of-the-art performance on widely used datasets Market1501 and MSMT17. Visualization of retrieval results illustrates the effectiveness and robustness of the proposed method.