Abstract
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match individuals across visible and infrared cameras without relying on any annotation. Given the significant gap across visible and infrared modality, estimating reliable cross-modality association becomes a major challenge in USVI-ReID. Existing methods usually adopt optimal transport to associate the intra-modality clusters, which is prone to propagating the local cluster errors, and also overlooks global instance-level relations. By mining and attending to the visible-infrared modality bias, this paper focuses on addressing cross-modality learning from two aspects: bias-mitigated global association and modality-invariant representation learning. Motivated by the camera-aware distance rectification in single-modality re-ID, we propose modality-aware Jaccard distance to mitigate the distance bias caused by modality discrepancy, so that more reliable cross-modality associations can be estimated through global clustering. To further improve cross-modality representation learning, a `split-and-contrast' strategy is designed to obtain modality-specific global prototypes. By explicitly aligning these prototypes under global association guidance, modality-invariant yet ID-discriminative representation learning can be achieved. While conceptually simple, our method obtains state-of-the-art performance on benchmark VI-ReID datasets and outperforms existing methods by a significant margin, validating its effectiveness.
Abstract (translated)
无监督可见光-红外重识别（USVI-ReID）旨在不依赖任何标注的情况下，在可见光和红外摄像机之间匹配个体。鉴于可见光与红外模态之间的显著差异，估计可靠的跨模态关联成为USVI-ReID的主要挑战之一。现有方法通常采用最优传输来关联单个模态下的聚类，这种做法容易传播局部集群误差，并且忽视了全局实例级关系。 本文通过挖掘和关注可见光-红外模态偏差，从两个方面解决了跨模态学习问题：减少偏差的全局关联与模态不变表示学习。受单一模态重识别中摄像机感知距离校正的启发，我们提出了一种模态感知的雅克距离（modality-aware Jaccard distance），用于缓解由模态差异引起的距离偏差，从而通过全局聚类估计出更可靠的跨模态关联。 为了进一步改进跨模态表示学习，设计了一种“分离并对比”策略来获取特定于每个模态的全局原型。通过在全局关联引导下明确对齐这些原型，可以实现既模态不变又具有身份区分能力的表示学习。尽管概念简单，本文提出的方法在基准VI-ReID数据集上取得了最先进的性能，并且显著优于现有方法，验证了其有效性。
URL
https://arxiv.org/abs/2512.07760
