Dual-Modality-Shared Learning and Label Refinement for Unsupervised Visible-Infrared Person ReID

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match a person across two modalities without annotations. Current research primarily addresses the modality gap by establishing cross-modality correspondences through matching algorithms and utilizing memory banks for contrastive learning. However, the inherent noise in pseudo labels and neglect of hard samples often limit the efficacy of cross-modality learning. In this article, we propose a dual-modality-shared learning and label refinement (DLLR) algorithm for USVI-ReID. First, we leverage a cluster similarity matching (CSM) module and a cluster relationship-based label refinement (CRLR) algorithm to create and refine pseudo labels. Then, we adopt a weighted modality-shared memory (WMM) to construct memory banks by jointly considering sample distribution and feature differences, thereby enhancing the effectiveness of cross-modality learning. Extensive experiments on three publicly available datasets validate the effectiveness of our proposed method, which outperforms state-of-the-art methods. The code is available at https://github.com/CharRic/DLLR .