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Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification

CVPR 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Supervised person re-identification · 403 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace global features with visibility-aware region featuresperson re-identification · Market-1501, γ=0.5R-1: 64.5 -> 70.9Helped
replace global features with visibility-aware region featuresperson re-identification · Market-1501, γ=0.5mAP: 44.4 -> 48.8Helped
replace global features with visibility-aware region featuresperson re-identification · DukeMTMC-reID, γ=0.5R-1: 65.0 -> 69.5Helped
use VPM instead of SFR with bilateral crop trainingPartial-REID · Partial-REIDR-1: +10.6%Helped
use VPM instead of SFR with top-crop trainingPartial-iLIDS · Partial-iLIDSR-1: +3.3%Helped
add triplet loss (ablation)VPM · Partial-iLIDSR-1: 57.1 -> 67.2Helped
add triplet loss (ablation)VPM · Market-1501mAP: 77.8 -> 80.8Helped
use visibility-aware shared regions instead of all region features at testing (ablation)VPM · Partial-iLIDSR-1: 63.0 -> 67.2Helped
use visibility-aware shared regions instead of all region features at testing (ablation)VPM · Market-1501mAP: 79.7 -> 80.8Helped
apply self-supervision to triplet-loss region selection (ablation)VPM · Partial-iLIDSR-1: 61.3 -> 67.2Helped
apply self-supervision to identity-loss region selection (ablation)VPM · Partial-iLIDSR-1: 58.8 -> 67.2Helped
apply self-supervision to both triplet and identity losses (ablation)VPM · Partial-iLIDSR-1: 59.7 -> 67.2Helped

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