Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification
arXiv:2405.16597 · doi:10.1109/ICASSP49660.2025.10890451
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.
Accepted by ICASSP 2025
References in corpus (6)
- Learning Discriminative Features with Multiple Granularities for Person Re-Identification
- Person Re-identification by Contour Sketch under Moderate Clothing Change
- Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
- Disentangled Representations for Short-Term and Long-Term Person Re-Identification
- Sampling Agnostic Feature Representation for Long-Term Person Re-identification
- Distribution Aligned Semantics Adaption for Lifelong Person Re-Identification