Unsupervised Person Re-Identification With Diffusion Model via Semantic-Aware Disentanglement Representation Learning

Unsupervised person re-identification (Re-ID) requires learning semantic representation without identity labels. Existing methods entangle identity-related person features with camera-related background features, hindering discriminative feature learning. Also, these methods often disrupt the semantic structure of the person, weakening the semantic representation. In this paper, we propose the Semantic-Aware Disentanglement Representation Learning (SDRL) framework with diffusion models for unsupervised person Re-ID. Firstly, to enhance feature learning, we propose the Disentanglement Aggregation Model (DAM). This model disentangles identity-related features from camera-related features to generate multi-view features. Secondly, to promote the consistency of multi-view features, we design the multi-view similarity consistency (MSC) loss to constrain intra-camera and cross-camera similarity distributions. Thirdly, to generate semantically meaningful patches, we propose the Semantic Spatial Diffusion Model (SSDM). This model operates on identity-related features to perform the denoising diffusion process over spatial transformer parameters. Finally, to further enhance the semantic representation of generated patches, we design the Semantic Decoupled Contrastive (SDC) loss to perceive the inherent semantic structure. Numerous experiments on three demanding datasets prove that our approach is superior to the current unsupervised Re-ID approaches. The source code will be publicly available at https://github.com/taoxuefong/SDRL-reid.