显著性引导擦除与门控正则化的遮挡行人重识别

Key Points

• This research aims to improve the feature discrimination and robustness in occluded pedestrian re-identification.
• Developed a multi-view learning framework integrating channel attention, significance guided erasure, and gated regularization.
• Utilized squeeze-and-excitation residual network (SE-ResNet) to adaptively enhance discriminative features.
• Implemented a Top-k significance guided soft erasure module to locate key areas and reduce overfitting.
• Applied gating regularization to impose entropy constraints on feature channel selection.
• Achieved a mean average precision (mAP) of 83.4% and Rank-1 accuracy of 92.8% on the Occluded-REID dataset.
• On the MSMT17 dataset, mAP reached 71.4% with Rank-1 accuracy of 89.6%, showing significant improvement over existing methods.
• Demonstrated strong performance on both synthetic and natural occlusion datasets, verifying the universality of the approach.