Dual representation modeling and progressive contrastive learning for unsupervised video person re-identification

Unsupervised video person re-identification (USL-VReID) aims to identify the same person across cameras without annotation. The key to this challenging task is to take advantage of the discriminative features of frames and tracklets. Most existing methods model tracklet features by mean average pooling and address the USL-VReID problem by using cluster centroid contrastive learning. However, the mean average pooling strategy and cluster centers primarily focus on commonality, overlooking divergencies. In this paper, we propose a Dual Representation Modeling and Progressive Contrastive Learning (DRMPCL) method to learn fine-grained features of the diversity distribution. Specifically, our approach explores the discriminant features of diversity distribution in both frame-level tracklet modeling and tracklet-level discrepant contrastive learning. Then a multi-level progressive learning strategy effectively optimizes the model from reliable to informative, and from commonality to divergence. Comprehensive experiments on two widely-used datasets demonstrate that DRMPCL outperforms the state-of-the-art methods.