  Cloth-changing person Re-IDentification (Re-ID) aims at recognizing the same
person with clothing changes across non-overlapping cameras. Conventional
person Re-ID methods usually bias the model's focus on cloth-related appearance
features rather than identity-sensitive features associated with biological
traits. Recently, advanced cloth-changing person Re-ID methods either resort to
identity-related auxiliary modalities (e.g., sketches, silhouettes, keypoints
and 3D shapes) or clothing labels to mitigate the impact of clothes. However,
relying on unpractical and inflexible auxiliary modalities or annotations
limits their real-world applicability. In this paper, we promote cloth-changing
person Re-ID by effectively leveraging abundant semantics present within
pedestrian images without the need for any auxiliaries. Specifically, we
propose the Content and Salient Semantics Collaboration (CSSC) framework,
facilitating cross-parallel semantics interaction and refinement. Our framework
is simple yet effective, and the vital design is the Semantics Mining and
Refinement (SMR) module. It extracts robust identity features about content and
salient semantics, while mitigating interference from clothing appearances
effectively. By capitalizing on the mined abundant semantic features, our
proposed approach achieves state-of-the-art performance on three cloth-changing
benchmarks as well as conventional benchmarks, demonstrating its superiority
over advanced competitors.
