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Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification

CVPR 2022 · Label-efficient and robust learning › Recognition and long-tail · approach: Cross-modality and occlusion re-identification · 31 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace Moco pre-training with UP-ReID pre-trainingMGN with ResNet50 · CUHK03mAP: 74.7 -> 85.3Helped
Replace Moco pre-training with UP-ReID pre-trainingSpCL · Market1501 (M, unsupervised)mAP: 72.2 -> 75.1Helped
Add global consistency constraint (ablation)UP-ReID pre-training · CUHK03mAP: 74.7 -> 79.1(+4.4)Helped
Add intrinsic contrastive constraint (ablation)UP-ReID pre-training · CUHK03mAP: 74.7 -> 81.4(+6.7)Helped
Add both global consistency and intrinsic contrastive constraints (ablation)UP-ReID pre-training · CUHK03mAP: 74.7 -> 85.3(+10.6)Helped
Add hard mining to ICC (ablation)UP-ReID pre-training · CUHK03mAP: 80.7 -> 85.3Helped
Set patch count to M=8 instead of M=4 or M=12 (ablation)UP-ReID ICC · CUHK03mAP: 81.3 -> 85.3; 80.7 -> 85.3Helped
Use random or online positive patch selection instead of horizontally symmetric positives (ablation)UP-ReID ICC · LUPerson pre-trainingpatch-wise contrastive loss convergenceHurt

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