Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification

• URL: http://arxiv.org/abs/2405.16597v1
• Date: Sun, 26 May 2024 15:17:28 GMT
• Title: Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification
• Authors: Qizao Wang, Xuelin Qian, Bin Li, Lifeng Chen, Yanwei Fu, Xiangyang Xue,
• Abstract summary: Cloth-changing person Re-IDentification aims at recognizing the same person with clothing changes across non-overlapping cameras. We propose the Content and Salient Semantics Collaboration 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.
• Score: 74.10897798660314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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