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Learning Disentangled Attribute Representations for Robust Pedestrian Attribute Recognition

AAAI 2022 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 35 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace shared pooled features with attribute-specific features learned by SSCA (ablation)DAFL on PA100k · PA100kmA: 80.71 -> 82.21Helped
Add group attention merging and group consistency loss (ablation)DAFL with SSCA on PA100k · PA100kmA: 82.21 -> 82.65Helped
Add semantic triplet loss (ablation)DAFL with SSCA on PA100k · PA100kmA: 82.21 -> 82.41Helped
Use group-shared spatial memory instead of separate memory for every attribute (ablation)DAFL with SSCA and triplet loss on PA100k · PA100kmA: 83.18 -> 83.54Helped
Use 2 rather than 1 cascaded SSCA module (ablation)DAFL on PA100k · PA100kmA: 82.82 -> 83.31Helped
Use 3 rather than 2 cascaded SSCA modules (ablation)DAFL on PA100k · PA100kmA: 83.31 -> 83.54Helped
Increase the SSCA cascade from 3 to 4 modules (ablation)DAFL on PA100k · PA100kmA: 83.54 -> 83.01Hurt
Increase the SSCA cascade from 3 to 5 modules (ablation)DAFL on PA100k · PA100kmA: 83.54 -> 83.13Hurt
Increase the SSCA cascade from 3 to 6 modules (ablation)DAFL on PA100k · PA100kmA: 83.54 -> 82.73Hurt
Use DAFL rather than the strongest reported baselinePETA · PETAmA: 86.97 -> 87.07Helped
Use DAFL rather than the strongest reported baselinePETA · PETAF1: 87.91 -> 86.40Hurt
Use DAFL rather than the strongest reported baselineRAPv1 · RAPv1mA: 83.69 -> 83.72Helped

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