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: this https URL.
Abstract (translated)
无监督可见光-红外行人重识别（USVI-ReID）的目标是从未标注的跨模态行人数据集中学习模态不变性图像特征，同时减少模态差距并尽量减少对成本高昂的人工注释的依赖。现有方法通常通过基于聚类的对比学习来解决 USVI-ReID 问题，这种方法用单个聚类中心表示一个人。然而，它们主要关注每个聚类内图像之间的共性而忽略了这些图像之间细微的区别。为了解决这一局限性，我们提出了一种层次身份学习（HIL）框架。 由于每个大聚类可能包含反映图间细微差异的多个较小子聚类，我们通过二次聚类为每个现有粗粒度聚类生成多个记忆库。此外，我们提出了多中心对比学习（MCCL），以细化表示方法，增强模内聚类并最小化跨模式差距。为了进一步提高跨模态匹配质量，我们设计了一种双向逆向选择传输（BRST）机制，通过进行伪标签的双向匹配来建立可靠的跨模态对应关系。 在 SYSU-MM01 和 RegDB 数据集上进行的大量实验表明，所提出的方法优于现有方法。源代码可在以下网址获取：此 https URL。
URL
https://arxiv.org/abs/2509.11587
