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arXiv · 2405.16597

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

Qizao Wang·Xuelin Qian·Bin Li·Lifeng Chen·Yanwei Fu·Xiangyang Xue
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Abstract

Cloth-changing person re-identification aims at recognizing the same person with clothing changes across non-overlapping cameras. Advanced methods either resort to identity-related auxiliary modalities (e.g., sketches, silhouettes, and keypoints) 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-identification by leveraging abundant semantics present within pedestrian images, without the need for any auxiliaries. Specifically, we first propose a unified Semantics Mining and Refinement (SMR) module to extract robust identity-related content and salient semantics, mitigating interference from clothing appearances effectively. We further propose the Content and Salient Semantics Collaboration (CSSC) framework to collaborate and leverage various semantics, facilitating cross-parallel semantic interaction and refinement. Our proposed method achieves state-of-the-art performance on three cloth-changing benchmarks, demonstrating its superiority over advanced competitors. The code is available at https://github.com/QizaoWang/CSSC-CCReID.

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Qizao Wang, Xuelin Qian, Bin Li, Lifeng Chen, Yanwei Fu, Xiangyang Xue. 2024-05-26. Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification. https://doi.org/10.1109/icassp49660.2025.10890451

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