Research map / Label-efficient and robust learning / Recognition and long-tail
Graph-Enhanced Attribute-Aware Modeling for Cloth-Changing Person Re-Identification
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Proposes a graph-enhanced, attribute-aware model for cloth-changing person re-identification that models attribute semantics jointly rather than as isolated labels.
Problem
Cloth-changing person re-identification, where clothing color and texture become unreliable cues for matching the same person across cameras and time.
Approach
Instead of relying on auxiliary clothing-invariant biometric cues (which may be noisy) or treating attribute semantics as isolated labels, models relationships among attributes with a graph to enhance attribute-aware representations.
Closest papers by meaning
- DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID (CVPR 2025 · similarity 0.91)
- Identity-Clothing Similarity Modeling for Unsupervised Clothing Change Person Re-Identification (CVPR 2025 · similarity 0.91)
- Dual-stream Relation-modeling Disentanglement for Cloth-Changing Person Re-Identification (AAAI 2026 · similarity 0.91)
- Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification (CVPR 2023 · similarity 0.90)
- DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identification (CVPR 2025 · similarity 0.87)
- Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions (CVPR 2024 · similarity 0.84)
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