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Synergistic optimization of camera-aware disentanglement and consistency learning for unsupervised person re-identification
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DOI：10.1016/j.neunet.2026.109448.png)
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Abstract
En 中文
• SCC framework breaks the feature-label vicious cycle via co-optimization. • Camera-aware disentanglement mitigates bias via dynamic prototype learning. • Confidence-aware neighbor-center consistency refines noisy pseudo-labels. • Quantitative analyses verify camera invariance and pseudo-label reliability.
Keywords:
Unsupervised person re-identification
Synergistic optimization
Camera-aware representation learning
Pseudo-label refinement
Contrastive learning
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