Papers › Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification
Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification
Peixian Hong, Tao Wu, AnCong Wu, Xintong Han, Wei-Shi Zheng
Recently, person re-identification (Re-ID) has achieved great progress. However, current methods largely depend on color appearance, which is not reliable when a person changes the clothes. Cloth-changing Re-ID is challenging since pedestrian images with clothes change exhibit large intra-class variation and small inter-class variation. Some significant features for identification are embedded in unobvious body shape differences across pedestrians. To explore such body shape cues for cloth-changing Re-ID, we propose a Fine-grained Shape-Appearance Mutual learning framework (FSAM), a two-stream framework that learns fine-grained discriminative body shape knowledge in a shape stream and transfers it to an appearance stream to complement the cloth-unrelated knowledge in the appearance features. Specifically, in the shape stream, FSAM learns fine-grained discriminative mask with the guidance of identities and extracts fine-grained body shape features by a pose-specific multi-branch network. To complement cloth-unrelated shape knowledge in the appearance stream, dense interactive mutual learning is performed across low-level and high-level features to transfer knowledge from shape stream to appearance stream, which enables the appearance stream to be deployed independently without extra computation for mask estimation. We evaluated our method on benchmark cloth-changing Re-ID datasets and achieved the start-of-the-art performance.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Person Re-Identification | LTCC | FSAM | Rank-1 | 38.5 | #8 of 13 | Archive leaderboard | report |
| Person Re-Identification | LTCC | FSAM | mAP | 16.2 | #8 of 13 | Archive leaderboard | report |
| Person Re-Identification | PRCC | FSAM | Rank-1 | 54.5 | #11 of 13 | Archive leaderboard | report |
| Person Re-Identification | VC-Clothes | FSAM | Rank-1 | 78.6 | #5 of 6 | Archive leaderboard | report |
| Person Re-Identification | VC-Clothes | FSAM | mAP | 78.9 | #5 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections