Contrastive Label-aware Spatial-Frequency Learning for Unsupervised Multi-modal Re-Identification

Unsupervised multi-modal re-identification (UMM-ReID) aims to learn identity-discriminative representations from multi-modalities by constructing pseudo-labels through clustering. However, it is difficult to obtain reliable and globally consistent pseudo-labels from multi-modal representations, and global pseudo-label supervision itself cannot clearly guide the representation learning and strengthen the focus on identity-discriminative regions. To address these issues, we propose Contrastive Label-aware Spatial-frequency Learning, which is a framework that alternates between global pseudo-label and minibatch representation learning. At the beginning of each epoch, we use adaptive spectral clustering and spectral gap-MDL criterion to generate global adaptive pseudo-labels for subsequent minibatch optimization. Based on pseudo-labels, cross-modal contrastive learning selectively adds reliable intra-modal negative relations to strengthen representation learning. In addition, we use pseudo-labels to guide spatial discrimination, and combine complementary frequency cues to enhance the learning of discriminative regions and suppress background noise. Experiments on real datasets fully demonstrate the effectiveness of this method.