Abstract
Unsupervised visible-infrared person re-identification (USL-VI-ReID) is of great research and practical significance yet remains challenging due to the absence of annotations. Existing approaches aim to learn modality-invariant representations in an unsupervised setting. However, these methods often encounter label noise within and across modalities due to suboptimal clustering results and considerable modality discrepancies, which impedes effective training. To address these challenges, we propose a straightforward yet effective solution for USL-VI-ReID by mitigating universal label noise using neighbor information. Specifically, we introduce the Neighbor-guided Universal Label Calibration (N-ULC) module, which replaces explicit hard pseudo labels in both homogeneous and heterogeneous spaces with soft labels derived from neighboring samples to reduce label noise. Additionally, we present the Neighbor-guided Dynamic Weighting (N-DW) module to enhance training stability by minimizing the influence of unreliable samples. Extensive experiments on the RegDB and SYSU-MM01 datasets demonstrate that our method outperforms existing USL-VI-ReID approaches, despite its simplicity. The source code is available at: this https URL.
Abstract (translated)
无监督的可见光-红外行人重识别（USL-VI-ReID）具有重要的研究和实用价值，但由于缺少标注信息而颇具挑战性。现有的方法旨在在无监督环境下学习模态不变表征。然而，这些方法常因次优的聚类结果及显著的模态差异，在同一或跨模态中遭遇标签噪声，阻碍了有效的训练过程。为解决这些问题，我们提出了一种通过利用邻域信息来缓解普遍存在的标签噪声的简单而有效的方法，以应对USL-VI-ReID中的挑战。具体来说，我们引入了基于邻居指导的通用标签校准（N-ULC）模块，在同质和异质空间中用从邻近样本衍生出的软标签替代显式的硬伪标签，以此来减少标签噪声。此外，我们还提出了基于邻居指导的动力权重调整（N-DW）模块，通过最小化不可靠样本的影响以增强训练稳定性。在RegDB和SYSU-MM01数据集上的广泛实验表明，尽管方法简单，我们的方法仍优于现有的USL-VI-ReID方案。源代码可在以下链接获取：此 https URL。
URL
https://arxiv.org/abs/2412.12220
