Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification

Haonan Shi; Yubin Wang; De Cheng; Lingfeng He; Nannan Wang; Xinbo Gao

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arXiv · 2509.11587

Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification

Haonan Shi·Yubin Wang·De Cheng·Lingfeng He·Nannan Wang·Xinbo Gao

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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.

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BibTeX RIS

Haonan Shi, Yubin Wang, De Cheng, Lingfeng He, Nannan Wang, Xinbo Gao. 2025-09-15. Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification. https://arxiv.org/abs/2509.11587

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