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

TL;DR

This paper introduces a novel framework for cloth-changing person re-identification that leverages intrinsic semantic information within pedestrian images, avoiding auxiliary data, and achieves state-of-the-art results.

Contribution

The paper proposes a unified semantics mining and refinement module and a collaboration framework to improve cloth-changing person re-ID without auxiliary modalities.

Findings

Achieves state-of-the-art performance on three benchmarks.

Effectively extracts identity-related content and salient semantics.

Outperforms existing methods relying on auxiliary data.

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.