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.

我们提出了 Content and Salient Semantics Collaboration（CSSC）框架，通过有效利用行人图像中存在的丰富语义而无需任何辅助手段，实现了衣物变换的人物重识别。我们的方法通过提取关于内容和显著语义的稳健身份特征，并有效地减少了服装外观的干扰，从而在三个衣物变换基准数据集以及传统基准数据集上取得了最先进的性能，显示了其优越性。