Papers › Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person...
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Qizao Wang, Xuelin Qian, Bin Li, xiangyang xue, Yanwei Fu
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate identity-relevant feature learning, including soft-biometrics features of shapes or gaits, and additional labels of clothing. However, this information may be unavailable in real-world applications. In this paper, we propose a novel FIne-grained Representation and Recomposition (FIRe²) framework to tackle both limitations without any auxiliary annotation or data. Specifically, we first design a Fine-grained Feature Mining (FFM) module to separately cluster images of each person. Images with similar so-called fine-grained attributes (e.g., clothes and viewpoints) are encouraged to cluster together. An attribute-aware classification loss is introduced to perform fine-grained learning based on cluster labels, which are not shared among different people, promoting the model to learn identity-relevant features. Furthermore, to take full advantage of fine-grained attributes, we present a Fine-grained Attribute Recomposition (FAR) module by recomposing image features with different attributes in the latent space. It significantly enhances robust feature learning. Extensive experiments demonstrate that FIRe² can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks. The code is available at https://github.com/QizaoWang/FIRe-CCReID.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Person Re-Identification | LTCC | FIRe2 | Rank-1 | 44.6 | #4 of 13 | Archive leaderboard | report |
| Person Re-Identification | LTCC | FIRe2 | mAP | 19.1 | #4 of 13 | Archive leaderboard | report |
| Person Re-Identification | PRCC | FIRe2 | Rank-1 | 65.0 | #5 of 13 | Archive leaderboard | report |
| Person Re-Identification | PRCC | FIRe2 | mAP | 63.1 | #5 of 13 | Archive leaderboard | report |
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