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Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity

CVPR 2016 · Probabilistic and scientific modeling › Molecules and proteins · approach: Statistical analysis of ML practice · 49 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Model AU intensity dependencies jointly with copula potentials and perform joint inferenceCOR ordinal regression · DISFAICC (3,1): 0.39 -> 0.46Helped
Model AU intensity dependencies jointly with copula potentials and perform joint inferenceCOR ordinal regression · PAINICC (3,1): 0.24 -> 0.33Helped
Model AU intensity dependencies jointly with copula potentials and perform joint inferenceCOR ordinal regression · DISFAMSE: 1.14 -> 1.01Helped
Model AU intensity dependencies jointly with copula potentials and perform joint inferenceCOR ordinal regression · PAINCORR: 0.26 -> 0.37Helped
Use a sparse lasso graph rather than a fully connected AU graph (ablation)COR · DISFAICC (3,1): 0.43 -> 0.46Helped
Use a sparse lasso graph rather than a fully connected AU graph (ablation)COR · PAINCORR: 0.33 -> 0.37Helped
Use a sparse lasso graph rather than a fully connected AU graph (ablation)COR · DISFAMSE: 1.15 -> 1.01Helped
Use the proposed two-step learning rather than direct optimization (ablation)Sparse-graph COR · DISFAICC (3,1): 0.45 -> 0.46Helped
Use the proposed two-step learning rather than direct optimization (ablation)Sparse-graph COR · PAINMSE: 1.17 -> 1.10Helped
Use the proposed two-step learning rather than direct optimization (ablation)Sparse-graph COR · PAINCORR: 0.37 -> 0.37No clear effect
Use joint inference rather than marginal inferenceCOR model · DISFA and PAINCORRHelped

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