Abstract
Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) offers a more flexible and cost-effective alternative compared to supervised methods. This field has gained increasing attention due to its promising potential. Existing methods simply cluster modality-specific samples and employ strong association techniques to achieve instance-to-cluster or cluster-to-cluster cross-modality associations. However, they ignore cross-camera differences, leading to noticeable issues with excessive splitting of identities. Consequently, this undermines the accuracy and reliability of cross-modal associations. To address these issues, we propose a novel Dynamic Modality-Camera Invariant Clustering (DMIC) framework for USL-VI-ReID. Specifically, our DMIC naturally integrates Modality-Camera Invariant Expansion (MIE), Dynamic Neighborhood Clustering (DNC) and Hybrid Modality Contrastive Learning (HMCL) into a unified framework, which eliminates both the cross-modality and cross-camera discrepancies in clustering. MIE fuses inter-modal and inter-camera distance coding to bridge the gaps between modalities and cameras at the clustering level. DNC employs two dynamic search strategies to refine the network's optimization objective, transitioning from improving discriminability to enhancing cross-modal and cross-camera generalizability. Moreover, HMCL is designed to optimize instance-level and cluster-level distributions. Memories for intra-modality and inter-modality training are updated using randomly selected samples, facilitating real-time exploration of modality-invariant representations. Extensive experiments have demonstrated that our DMIC addresses the limitations present in current clustering approaches and achieve competitive performance, which significantly reduces the performance gap with supervised methods.
Abstract (translated)
无监督学习可见光-红外行人重识别（USL-VI-ReID）相比有监督方法提供了更灵活且成本更低的替代方案。由于其潜在的应用前景，这一领域受到了越来越多的关注。现有方法简单地对模态特定样本进行聚类，并采用强大的关联技术实现实例到集群或集群到集群的跨模态关联。然而，它们忽略了跨摄像机差异，导致身份过度分割的问题显著存在。这削弱了跨模态关联的准确性和可靠性。为了解决这些问题，我们提出了一种新的动态模态-摄像机不变聚类（DMIC）框架用于USL-VI-ReID。具体来说，我们的DMIC自然地将模态-摄像机不变扩展（MIE）、动态邻域聚类（DNC）和混合模态对比学习（HMCL）整合到一个统一的框架中，这消除了聚类中的跨模态和跨摄像机差异。MIE融合了跨模态和跨摄像机的距离编码，在聚类层面弥合了模态和摄像机之间的差距。DNC采用了两种动态搜索策略来优化网络的目标函数，从提高可区分性转变为增强跨模态和跨摄像机的泛化能力。此外，HMCL旨在优化实例级别和集群级别的分布。使用随机选择的样本更新模态内和跨模态训练的记忆，有助于实时探索模态不变表征。广泛的实验表明，我们的DMIC解决了当前聚类方法中存在的局限性，并实现了具有竞争力的表现，显著缩小了与监督方法之间的性能差距。
URL
https://arxiv.org/abs/2412.08231
