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Research map / Label-efficient and robust learning / Self-supervised and contrastive

Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition

CVPR 2022 · Label-efficient and robust learning › Self-supervised and contrastive · approach: Empirical contrastive visual learning · 41 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use FACS-guided local representations and contrastive and predicting learning instead of generic self-supervised featuresAU recognition · BP4DF1 score (Avg.): 56.1 -> 61.6Helped
Use FACS-guided local representations and contrastive and predicting learning instead of generic self-supervised featuresAU recognition · DISFAF1 score (Avg.): 45.0 -> 60.2Helped
Use the proposed learned representations and fine-tune the encoderAU recognition · BP4DF1 score (Avg.): 64.1 -> 64.5Helped
Use the proposed learned representations and fine-tune the encoderAU recognition · DISFAF1 score (Avg.): 63.8 -> 64.5Helped
Add intra-area instance discrimination loss (λ = 0.1) to the inter-area contrastive objective (ablation)Knowledge-driven self-supervised AU representation learning · BP4DF1 score: increases by 7.6%Helped
Add intra-area instance discrimination loss (λ = 0.1) to the inter-area contrastive objective (ablation)Knowledge-driven self-supervised AU representation learning · DISFAF1 score: increases by 8.6%Helped
Increase the intra-area loss weight beyond λ = 0.1 (ablation)Knowledge-driven self-supervised AU representation learning · BP4D and DISFAF1 scoreHurt
Add the contrastive learning component to predicting learning (α = 0.01) (ablation)Knowledge-driven self-supervised AU representation learning · BP4D and DISFAF1 scoreHelped
Increase the contrastive component weight beyond α = 0.01 (ablation)Knowledge-driven self-supervised AU representation learning · BP4D and DISFAF1 scoreHurt

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