Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a
promising yet challenging retrieval task. The key challenges in USL-VI-ReID are
to effectively generate pseudo-labels and establish pseudo-label
correspondences across modalities without relying on any prior annotations.
Recently, clustered pseudo-label methods have gained more attention in
USL-VI-ReID. However, previous methods fell short of fully exploiting the
individual nuances, as they simply utilized a single memory that represented an
identity to establish cross-modality correspondences, resulting in ambiguous
cross-modality correspondences. To address the problem, we propose a
Multi-Memory Matching (MMM) framework for USL-VI-ReID. We first design a
Cross-Modality Clustering (CMC) module to generate the pseudo-labels through
clustering together both two modality samples. To associate cross-modality
clustered pseudo-labels, we design a Multi-Memory Learning and Matching (MMLM)
module, ensuring that optimization explicitly focuses on the nuances of
individual perspectives and establishes reliable cross-modality
correspondences. Finally, we design a Soft Cluster-level Alignment (SCA) module
to narrow the modality gap while mitigating the effect of noise pseudo-labels
through a soft many-to-many alignment strategy. Extensive experiments on the
public SYSU-MM01 and RegDB datasets demonstrate the reliability of the
established cross-modality correspondences and the effectiveness of our MMM.
The source codes will be released.

提出了一种用于可见 - 红外无监督人物再识别的多内存匹配框架，通过生成伪标签和建立不依赖于任何先前注释的多模态伪标签对应关系来解决该任务中的关键挑战。通过使用交叉模态聚类、多内存学习和匹配以及软聚类级对齐策略来改进现有方法的不足，提高了跨模态对应关系的可靠性和效果。