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Pose-Guided Feature Disentangling for Occluded Person Re-identification Based on Transformer

AAAI 2022 · 3D vision › Human pose estimation · approach: Human shape and pose regression · 231 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add PFA (ablation)PFD encoder-decoder · Occluded-DukeRank-1: 58.2 -> 63.7Helped
Add PFA (ablation)PFD encoder-decoder · Occluded-DukemAP: 48.3 -> 56.2Helped
Add PVM (ablation)PFD encoder-decoder · Occluded-DukeRank-1: 58.2 -> 62.4Helped
Add PVM (ablation)PFD encoder-decoder · Occluded-DukemAP: 48.3 -> 54.6Helped
Add PFA and PVM together (ablation)PFD encoder-decoder · Occluded-DukeRank-1: 58.2 -> 67.0Helped
Add PFA and PVM together (ablation)PFD encoder-decoder · Occluded-DukemAP: 48.3 -> 59.5Helped
Add Pose-guided Push Loss to PFA+PVM (ablation)PFD · Occluded-DukeRank-1: 67.0 -> 67.7Helped
Add Pose-guided Push Loss to PFA+PVM (ablation)PFD · Occluded-DukemAP: 59.5 -> 60.1Helped
Increase semantic-view count from 1 to 17 (ablation)PFD · Occluded-DukeRank-1: 65.5 -> 67.7Helped
Increase semantic-view count from 1 to 17 (ablation)PFD · Occluded-DukemAP: 57.1 -> 60.1Helped
Increase semantic-view count from 17 to 20 (ablation)PFD · Occluded-DukeRank-1: 67.7 -> 66.9Hurt
Increase semantic-view count from 17 to 20 (ablation)PFD · Occluded-DukemAP: 60.1 -> 59.0Hurt

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