DPM++: Dynamic Masked Metric Learning for Occluded Person Re-identification
Although person re-identification has made impressive progress, occlusion caused by obstacles remains an unsettled issue in real applications. The difficulty lies in the mismatch between incomplete occluded samples and holistic identity representations. Severe occlusion removes discriminative body cues and introduces interference from background clutter and occluders, making global metric learning unreliable. Existing methods mainly rely on extra pre-trained models to estimate visible parts for alignment or construct occluded samples via data augmentation, but still lack a unified framework that learns robust visibility-consistent matching under realistic occlusion patterns. In this paper...
| Authors | Lei Tan, Yingshi Luan, Pincong Zou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 3 June 2026 |
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