Abstract
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified people in infrared images to visible images without annotation, and vice versa. USVI-ReID is a challenging yet under-explored task. Most existing methods address the USVI-ReID problem using cluster-based contrastive learning, which simply employs the cluster center as a representation of a person. However, the cluster center primarily focuses on shared information, overlooking disparity. To address the problem, we propose a Progressive Contrastive Learning with Multi-Prototype (PCLMP) method for USVI-ReID. In brief, we first generate the hard prototype by selecting the sample with the maximum distance from the cluster center. This hard prototype is used in the contrastive loss to emphasize disparity. Additionally, instead of rigidly aligning query images to a specific prototype, we generate the dynamic prototype by randomly picking samples within a cluster. This dynamic prototype is used to retain the natural variety of features while reducing instability in the simultaneous learning of both common and disparate information. Finally, we introduce a progressive learning strategy to gradually shift the model's attention towards hard samples, avoiding cluster deterioration. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method. PCLMP outperforms the existing state-of-the-art method with an average mAP improvement of 3.9%. The source codes will be released.
Abstract (translated)
无监督可见-红外人员识别（USVI-ReID）旨在将红外图像中指定的个人与可见图像中的个人进行匹配，反之亦然。USVI-ReID是一个具有挑战性但尚未被充分探索的任务。现有的方法主要通过基于聚类的对比学习来解决USVI-ReID问题，这简单地使用聚类中心作为一个人的表示。然而，聚类中心主要关注共享信息，忽视了差异。为了解决这个问题，我们提出了一个渐进式对比学习多原型（PCLMP）方法来解决USVI-ReID问题。简而言之，我们首先通过选择距离聚类中心最远的样本生成硬原型。这个硬原型用于对比损失，强调差异。此外，我们通过随机选择聚类内的样本生成动态原型。这个动态原型用于保留特征的自然多样性的同时，减少同时学习共同信息和差异信息的不稳定性。最后，我们引入了渐进学习策略，逐渐将模型的注意力从普通样本转移到困难样本，避免聚类恶化。在公开可用的大规模数据集SYSU-MM01和RegDB上进行的大量实验证实了所提出方法的有效性。PCLMP平均mAP提高了3.9%。源代码将发布。
URL
https://arxiv.org/abs/2402.19026
