Two-stage contrastive learning for unsupervised visible-infrared person re-identification

Unsupervised visible-infrared person re-identification (USVI-ReID) plays a crucial role in computer vision. The key challenge of USVI-ReID is to learn the discriminative features of images and establish cross-modal correspondence without using class labels. We propose a two-stage contrastive learning method for USVI-ReID. The first stage is instance-wise contrastive learning for learning a discriminative model. The learned discriminative model is transferred to the second stage for clustering operation, thus forming category-level supervision and promoting the execution of cluster-wise contrastive learning. Besides, a progressive training strategy is proposed to gradually shift the model's attention from instances to clusters. Extensive experiments on two public datasets SYSU-MM01 and RegDB demonstrate the effectiveness of the proposed method.