Unsupervised Visible-Infrared Person ReID via Modality-Camera Balance Label Refinement

Unsupervised Learning Visible-Infrared Person Re-Identification (USL-VI-ReID) focuses on developing a cross-modality retrieval model without the need for labels, minimizing the dependence on costly manual annotation across modalities. Recently, various approaches focus on reducing the cross-modality discrepancies. However, they ignore that USL-VI-ReID is also a task of solving discrepancies while exploring fine-grained information in hierarchical domains. In this article, we propose a hierarchical Modality-Camera Balance Label Refinement (MCBL) framework to balance the contributions of each camera-modality. Meanwhile, we explore the fine-grained features and refine the noise labels at each training stages. Specifically, our MCBL naturally combines Modality-Camera Balanced Label Mining (MBLM), Unreliable Pseudo-Label Re-align (UPR), and Hybrid Modality-Camera Contrastive Learning (HMCCL) into a unified framework, which balances the association information for each hierarchical domain through refining noise labels. Technically, MBLM filters cluster-level noise samples utilizing a modality-camera balance strategy, thereby ensuring that reliable samples are stored in memory for effective contrast learning. UPR refines the noise labels through the re-alignment methods at the instance level, thus improving the accuracy of labels and further enhancing the model’s generalization ability. Moreover, the key of HMCCL is optimizing the distribution at both the instance and cluster levels, which forces the sample to be close to its cluster proxy while being far from others in a real-time memory update phase. Extensive experiments have shown that our MCBL addresses the current limitations of camera discrepancy and achieves competitive performance.