Adaptive Pseudo-Label Purification and Debiasing for Unsupervised Visible-Infrared Person Re-Identification

Unsupervised Visible-Infrared Person Re-Identification (USVI-ReID) aims to match visible and infrared person images without relying on prior annotations. Recently, unsupervised contrastive learning methods have become the mainstream approach for USVI-ReID, leveraging clustering algorithms to generate pseudo-labels. However, these methods often suffer from inherent noisy pseudo-labels, which significantly hinders their performance. To address this challenge, we propose a Adaptive Pseudo-label Purification and Debiasing (APPD) framework for USVI-ReID, which is designed to calibrate noisy pseudo-labels and dynamically detects clean pseudo-labels, thereby enhancing the model’s performance and reliability. Specifically, we propose an Adaptive Pseudo-label Calibration and Division (APCD) module, which calibrates noisy pseudo-labels by assessing their reliability and divides pseudo-labels into clean and noisy subsets, ensuring a more focused and accurate learning process. Based on the calibrated pseudo-labels, we develop an Optimal Transport Prototype Matching (OTPM) module to establish robust cross-modality correspondences. For clean pseudo-labels, we propose a Debiased Memory Hybrid Learning (DMHL) module, which jointly captures modality-specific and modality-invariant information while addressing sampling bias to enhance feature representation. To effectively utilize noisy pseudo-labels, we introduce a Neighbor Relation Learning (NRL) module that mitigates intra-class variations by exploring neighbor relationships in the feature space. Comprehensive experiments conducted on two widely recognized USVI-ReID benchmarks demonstrate that APPD achieves state-of-the-art performance, significantly outperforming existing methods.