{"ID":2870742,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11587","arxiv_id":"2509.11587","title":"Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification","abstract":"Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modality gap while minimizing reliance on costly manual annotations. Existing methods typically address USVI-ReID using cluster-based contrastive learning, which represents a person by a single cluster center. However, they primarily focus on the commonality of images within each cluster while neglecting the finer-grained differences among them. To address the limitation, we propose a Hierarchical Identity Learning (HIL) framework. Since each cluster may contain several smaller sub-clusters that reflect fine-grained variations among images, we generate multiple memories for each existing coarse-grained cluster via a secondary clustering. Additionally, we propose Multi-Center Contrastive Learning (MCCL) to refine representations for enhancing intra-modal clustering and minimizing cross-modal discrepancies. To further improve cross-modal matching quality, we design a Bidirectional Reverse Selection Transmission (BRST) mechanism, which establishes reliable cross-modal correspondences by performing bidirectional matching of pseudo-labels. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed method outperforms existing approaches. The source code is available at: https://github.com/haonanshi0125/HIL.","short_abstract":"Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modality gap while minimizing reliance on costly manual annotations. Existing methods typically address USVI-ReID using cluster-based contrastive...","url_abs":"https://arxiv.org/abs/2509.11587","url_pdf":"https://arxiv.org/pdf/2509.11587v1","authors":"[\"Haonan Shi\",\"Yubin Wang\",\"De Cheng\",\"Lingfeng He\",\"Nannan Wang\",\"Xinbo Gao\"]","published":"2025-09-15T05:10:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":609791,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2870742,"paper_url":"https://arxiv.org/abs/2509.11587","paper_title":"Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification","repo_url":"https://github.com/haonanshi0125/HIL","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}