Abstract
Unsupervised visible-infrared person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations for model learning. Previous methods unify pseudo-labels of cross-modality images through label association algorithms and then design contrastive learning framework for global feature learning. However, these methods overlook the cross-modality variations in feature representation and pseudo-label distributions brought by fine-grained patterns. This insight results in insufficient modality-shared learning when only global features are optimized. To address this issue, we propose a Semantic-Aligned Learning with Collaborative Refinement (SALCR) framework, which builds up optimization objective for specific fine-grained patterns emphasized by each modality, thereby achieving complementary alignment between the label distributions of different modalities. Specifically, we first introduce a Dual Association with Global Learning (DAGI) module to unify the pseudo-labels of cross-modality instances in a bi-directional manner. Afterward, a Fine-Grained Semantic-Aligned Learning (FGSAL) module is carried out to explore part-level semantic-aligned patterns emphasized by each modality from cross-modality instances. Optimization objective is then formulated based on the semantic-aligned features and their corresponding label space. To alleviate the side-effects arising from noisy pseudo-labels, we propose a Global-Part Collaborative Refinement (GPCR) module to mine reliable positive sample sets for the global and part features dynamically and optimize the inter-instance relationships. Extensive experiments demonstrate the effectiveness of the proposed method, which achieves superior performances to state-of-the-art methods. Our code is available at \href{this https URL}.
Abstract (translated)
无监督可见光-红外行人重识别（USL-VI-ReID）旨在匹配不同模态下同一行人的图像，而不依赖于人工标注以训练模型。以往的方法通过标签关联算法统一跨模态图像的伪标签，并设计对比学习框架进行全局特征学习。然而，这些方法忽视了由细粒度模式带来的跨模态在特征表示和伪标签分布上的变化。这种认识导致仅优化全局特征时模态共享学习不足。为解决这一问题，我们提出了一种语义对齐协作细化（SALCR）框架，该框架建立了一个针对每个模态强调的特定细粒度模式进行优化的目标，从而实现不同模态之间标签分布的互补对齐。 具体来说，首先引入了双关联全局学习（DAGI）模块以双向方式统一跨模态实例的伪标签。随后，执行细粒度语义对齐学习（FGSAL）模块，从跨模态实例中探索每个模态强调的部分级语义对齐模式。然后基于语义对齐特征及其对应的标签空间来制定优化目标。 为了缓解由于噪声伪标签带来的副作用，我们提出了一种全局-部分协作细化（GPCR）模块，该模块动态挖掘出对于全局和局部特征来说可靠的正样本集，并优化实例间的关联关系。 广泛的实验表明了所提出方法的有效性，在无监督可见光-红外行人重识别任务上达到了优于当前最优方法的性能。我们的代码可在\href{this https URL}获取。
URL
https://arxiv.org/abs/2504.19244
