Instance-Cluster Cross-Clothing Supervision for Sparse Unsupervised Clothing Change Person Re-Identification

Clothing change person re-identification (CC-ReID) aims to match images of the same person captured under different clothing conditions. To reduce annotation costs, recent studies focus on unsupervised CC-ReID (UCC-ReID), which typically employs clustering and matching algorithms to generate identity pseudo-labels and associate images of the same person wearing different clothing. Although existing UCC-ReID methods substantially reduce the need for manual annotation, they still rely on a stringent dense identity assumption: the vast majority of individuals in the training set are assumed to have images captured in more than one outfit. However, this assumption is often difficult to satisfy in many public scenarios. To this end, we not only consider the dense scenario but also explore the more challenging sparse scenario, in which only a small proportion of identities in the training set have images captured in more than one outfit. We propose an instance-cluster cross-clothing supervision (ICCS) framework, which elegantly generates cross-clothing supervision signals at both the cluster and instance levels. At the instance level, ICCS introduces the random channel augmentation and random clothing fusion modules to transform the clothing regions of person images, thereby enhancing the model's robustness to clothing variations. At the cluster level, ICCS introduces an identity-clothing mutual supervision module, which enables the class vectors of original and clothing-masked images to mutually guide each other, thereby providing accurate cross-clothing supervision signals for optimization. Experimental results on multiple CC-ReID benchmarks under both dense and sparse scenarios demonstrate that the proposed ICCS significantly outperforms existing state-of-the-art methods in overall performance.