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Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification

AAAI 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Cross-modality and occlusion re-identification · 176 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use uncertainty to reweight pseudo-label ID classification loss (ablation)SBase · DukeMTMC→Market-1501mAP: 75.4 -> 77.3Helped
Use uncertainty to reweight pseudo-label ID classification loss (ablation)SBase · Market-1501→DukeMTMCmAP: 64.8 -> 68.2Helped
Use uncertainty to reweight triplet loss (ablation)SBase · DukeMTMC→Market-1501mAP: 75.4 -> 76.1Helped
Use uncertainty to reweight triplet loss (ablation)SBase · Market-1501→DukeMTMCmAP: 64.8 -> 66.3Helped
Apply uncertainty weighting across ID, triplet, and contrastive losses (ablation)SBase · DukeMTMC→Market-1501mAP: 75.4 -> 78.1Helped
Apply uncertainty weighting across ID, triplet, and contrastive losses (ablation)SBase · Market-1501→DukeMTMCmAP: 64.8 -> 69.1Helped
Remove the uncertainty regularization loss (ablation)UNRN · DukeMTMC→Market-1501mAP: 77.5 -> 78.1Hurt
Remove the uncertainty regularization loss (ablation)UNRN · Market-1501→DukeMTMCmAP: 68.0 -> 69.1Hurt
Estimate uncertainty from feature distance rather than soft multilabel inconsistency (ablation)UNRN · DukeMTMC→Market-1501mAP: 76.5 vs 78.1Hurt
Use source centers only as uncertainty references (ablation)UNRN · DukeMTMC→Market-1501mAP: 76.0 vs 78.1Hurt
Use target cluster centers only as uncertainty references (ablation)UNRN · DukeMTMC→Market-1501mAP: 75.4 -> 77.8Helped
Increase memory bank size from 0 to 8192 instances (ablation)Baseline + Source + Contrastive loss · Market-1501→DukeMTMCmAP: 62.8 -> 64.8Helped

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