Text-Guided Multiround Learning for Unsupervised Visible–Infrared Person Re-Identification

Unsupervised Visible-Infrared Person Re-Identification (USVI-ReID) aims to match images of the same individual across visible and infrared modalities without relying on identity annotations. This task is critical in Visual Internet of Things (VIoT) systems. Existing methods typically generate pseudo-labels through clustering and cross-modality associations. However, the quality of these pseudo-labels is often compromised by unstable clustering performance, modality discrepancies, and unreliable matching strategies, resulting in suboptimal accuracy. Therefore, obtaining more reliable and robust pseudo-labels remains challenging in this domain. To address these challenges, we propose a Text-Guided Multi-Round Learning (TGMRL) framework that enhances the reliability and robustness of cross-modality pseudo-label associations. TGMRL comprises two core components: (1) the Text Dual-Cross Similarity Matching (TDSM) module, which facilitates cross-modality cluster alignment by constructing both visual and text-based cluster centers and integrating their similarity matrices for more accurate correspondence; and (2) the Multi-Round Pseudo-Label Guidance (MRPG) module, which enhances label consistency by imposing temporal regularization across clustering iterations through both similarity and Intersection-Over-Union (IoU) based measures. Extensive experiments on two benchmark datasets demonstrate that TGMRL significantly improves the reliability of pseudo-labels and achieves state-of-the-art performance on the USVI-ReID task.