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Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

AAAI 2020 · Graph learning › Knowledge and temporal graphs · approach: Graph reasoning for visual recognition · 53 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add dynamic semantic correspondence convolutions to the heatmap regression networkResNet-Deconv · BP4DICC: .67 -> .72Helped
add dynamic semantic correspondence convolutions to the heatmap regression networkResNet-Deconv · DISFAICC: .40 -> .47Helped
add SCC modules (ablation)SCC-Heatmap with 3 deconvolutional layers and 64×64 heatmap · DISFAICC: .40 -> .47Helped
add SCC modules (ablation)SCC-Heatmap with 3 deconvolutional layers and 64×64 heatmap · DISFAMAE: .21 -> .20Helped
use 3 deconvolutional layers and a 64×64 heatmap rather than 2 layers and a 32×32 heatmap (ablation)SCC-Heatmap · DISFAICC: .44 -> .47Helped
use 3 deconvolutional layers and a 64×64 heatmap rather than 2 layers and a 32×32 heatmap (ablation)SCC-Heatmap · DISFAMAE: .23 -> .20Helped
include both the center feature fᵢ and local difference fⱼ−fᵢ in edge features (ablation)SCC-Heatmap · DISFAICC: .43 -> .47; .38 -> .47Helped
include both the center feature fᵢ and local difference fⱼ−fᵢ in edge features (ablation)SCC-Heatmap · DISFAMAE: .22 -> .20; .21 -> .20Mixed
increase the number of nearest neighbors k (ablation)SCC-Heatmap · BP4D and DISFAICCHurt

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