Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-Identification

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims to learn modality-invariant features from unlabeled cross-modality data. However, existing approaches lack comprehensive cross-modality clustering or excessively pursue cluster-level association, which hinders reliable learning of modality-invariant features. To address these challenges, we propose an Extended Cross-Modality United Learning (ECUL) framework, which integrates Extended Modality-Camera Clustering (EMCC) and Two-Step Memory Updating Strategy (TSMem) modules. Specifically, we design ECUL to naturally unify intra-modality clustering, inter-modality clustering, and inter-modality instance selection, establishing compact and accurate cross-modality associations while reducing the introduction of noisy labels. Moreover, EMCC captures and filters neighborhood relationships by extending the encoding vector, which further promotes the learning of modality-invariant and camera-invariant knowledge in terms of the clustering algorithm. Finally, TSMem provides accurate and generalized proxy points for contrastive learning by updating memory in stages. Comprehensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed ECUL framework shows promising performance and even outperforms certain supervised methods.