Unsupervised Visible-Infrared ReID via Pseudo-Label Correction and Modality-Level Alignment

Unsupervised visible-infrared person reidentification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments without labeling. Previous methods utilize intramodality clustering and cross-modality feature matching to achieve UVI-ReID. However, there exist two challenges: 1) noisy pseudo-labels might be generated in the clustering process and 2) the cross-modality feature alignment via matching the marginal distribution of visible and infrared modalities may misalign the different identities from the two modalities. In this article, we first conduct a theoretical analysis where an interpretable generalization upper bound is introduced. Based on the analysis, we then propose a novel unsupervised cross-modality person reidentification framework (PRAISE). Specifically, to address the first challenge, we propose a pseudo-label correction (PLC) strategy that utilizes a beta mixture model (BMM) to predict the probability of misclustering-based network's memory effect and rectifies the correspondence by adding a perceptual term to contrastive learning. Next, we introduce a modality-level alignment (MLA) strategy that generates paired visible-infrared latent features and reduces the modality gap by aligning the labeling function of visible and infrared features to learn identity-discriminative and modality-invariant features. Experimental results on two benchmark datasets demonstrate that our method achieves a state-of-the-art (SOTA) performance than the unsupervised visible-ReID methods.