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Discovering Relationships Between Object Categories via Universal Canonical Maps

CVPR 2021 · 3D vision › Face and shape · approach: Articulated and non-rigid shape learning · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use ZoomOut initialization rather than random initializationUniversal canonical map training · DensePose-LVISAP: 25.4→35.1Helped
Use ZoomOut initialization rather than random initializationUniversal canonical map training · DensePose-LVISGErr: 23.7 → 28.0Hurt
Train on the expanded DensePose-LVIS v1.0 dataUniversal canonical map · DensePose-LVIS v1.0AP: 35.1→37.4Helped
Add m2m cycle consistency (ablation)Universal canonical map · Animals onlyAP: 37.5→38.2Helped
Add m2m cycle consistency (ablation)Universal canonical map · Animals onlyGErr: 14.05 → 11.96Helped
Add i2m image-to-mesh cycle consistency (ablation)Universal canonical map · Animals onlyAP: 37.5→38.1Helped
Add i2m image-to-mesh cycle consistency (ablation)Universal canonical map · Animals onlyGErr: 14.05 → 12.67Helped
Apply i2m across categories (ablation)Universal canonical map · Animals onlyAP: 37.5→38.3Helped
Add m2m and i2m cycle consistency (ablation)Universal canonical map · Animals onlyGErr: 14.05 → 11.37Helped
Add m2m and apply i2m across categories (ablation)Universal canonical map · Animals onlyAP: 37.5→38.5Helped
Add m2m and apply i2m across categories (ablation)Universal canonical map · Animals onlyGErr: 14.05 → 10.90Helped
Add m2m rather than using the baseline (ablation)Universal canonical map · PASCAL VOC keypoint transfer within training animal categoriesPCK-Transfer: 47.1→46.8Hurt

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