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Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation

CVPR 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Uncertainty-aware facial expression recognition · 59 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use KBSS with peak and valley annotations plus domain-knowledge supervisionAU intensity estimation · FERA 2015ICC(3,1): .66 -> .67Helped
Use KBSS with peak and valley annotations plus domain-knowledge supervisionAU intensity estimation · FERA 2015MAE: .68 -> .66Helped
Use KBSS with peak and valley annotations plus domain-knowledge supervisionAU intensity estimation · DISFAICC(3,1): .50 -> .36Hurt
Use KBSS with peak and valley annotations plus domain-knowledge supervisionAU intensity estimation · DISFAMAE: .30 -> .33Hurt
Add temporal intensity ordering (ablation)KBSS · FERA 2015ICC(3,1): .61 -> .67Helped
Add relative appearance similarity (ablation)KBSS · FERA 2015ICC(3,1): .62 -> .67Helped
Add facial symmetry (ablation)KBSS · FERA 2015ICC(3,1): .64 -> .67Helped
Add contrastive appearance difference (ablation)KBSS · FERA 2015ICC(3,1): .65 -> .67Helped
Use 5-element tuples rather than pairwise intensity relations (ablation)KBSS · FERA 2015ICC(3,1): .62 -> .67Helped
Use 5-element tuples rather than triplets (ablation)KBSS · FERA 2015ICC(3,1): .62 -> .67Helped
Add temporal intensity ordering (ablation)KBSS · DISFAMAE: .54 -> .33Helped
Add relative appearance similarity (ablation)KBSS · DISFAMAE: .51 -> .33Helped

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