Synergistic optimization of camera-aware disentanglement and consistency learning for unsupervised person re-identification.

Abstract

Unsupervised Person Re-Identification (ReID) is fundamentally challenged by a self-perpetuating vicious cycle, where camera-induced feature biases lead to unreliable pseudo-labels, which in turn corrupt the representation learning process. To break this cycle, this paper introduces the Synergistic Camera-aware Disentanglement and Consistency (SCC) optimization framework. Instead of tackling feature learning and label refinement in isolation, SCC establishes a virtuous cycle of mutual reinforcement between them. The framework begins by learning camera-robust representations through a camera-aware disentanglement mechanism, which mitigates camera bias by dynamically constructing disentangled prototypes. This process yields a more reliable feature space, providing a solid foundation for pseudo-labeling. Subsequently, a neighbor-center consistency optimization strategy leverages both local sample affinities and global cluster structures to significantly enhance the quality of these pseudo-labels. Critically, these refined labels serve as higher-quality supervision, guiding the representation learning in the next iteration to become even more discriminative. Through this synergistic co-optimization, SCC iteratively enhances both feature quality and label reliability. Extensive experiments on challenging datasets, including Market-1501 and MSMT17, demonstrate that our method significantly outperforms state-of-the-art approaches. Furthermore, quantitative analyses utilizing a camera-probe linear test, pseudo-label quality trajectories, and an early-vs-late stability study confirm that camera-invariant features and refined pseudo-labels reinforce each other across training, validating the effectiveness of our synergistic learning paradigm.