Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to retrieve pedestrian images of the same identity from different modalities without annotations. While prior work focuses on establishing cross-modality pseudo-label associations to bridge the modality-gap, they ignore maintaining the instance-level homogeneous and heterogeneous consistency in pseudo-label space, resulting in coarse associations. In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations. It models both homogeneous and heterogeneous affinities, leveraging them to define the inconsistency for the pseudo-labels and then minimize it, leading to pseudo-labels that maintain alignment across modalities and consistency within intra-modality structures. Additionally, a straightforward plug-and-play Online Cross-memory Label Refinement (OCLR) module is proposed to further mitigate the impact of noisy pseudo-labels while simultaneously aligning different modalities, coupled with a Modality-Invariant Representation Learning (MIRL) framework. Experiments demonstrate that our proposed method outperforms existing USL-VI-ReID methods, highlighting the superiority of our MULT in comparison to other cross-modality association methods. The code will be available.

翻译：无监督可见光-红外行人重识别（USL-VI-ReID）旨在无标注条件下从不同模态中检索同一身份的行人图像。现有工作虽聚焦于建立跨模态伪标签关联以弥合模态差异，却忽视了在伪标签空间中维持实例级别的同质与异质一致性，导致关联精度不足。为此，我们提出模态统一标签迁移（MULT）模块，该模块同时考虑同质与异质细粒度实例级结构，生成高质量的跨模态标签关联。通过建模同质与异质亲和度，进而定义伪标签的不一致性并最小化该不一致性，最终得到在模态间保持对齐且模态内结构保持一致的伪标签。此外，我们提出简洁的即插即用在线跨存储标签精炼（OCLR）模块，在缓解噪声伪标签影响的同时对齐不同模态，并联合模态不变表示学习（MIRL）框架。实验表明，所提方法优于现有USL-VI-ReID方法，充分验证了MULT相较于其他跨模态关联方法的优越性。代码将开源。