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Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition

CVPR 2023 · Video › Video segmentation and tracking · approach: Deepfake video detection · 107 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use M3DFEL instead of NR-DFERNetDFER · DFEWWAR: 68.19 -> 69.25Helped
Use M3DFEL instead of NR-DFERNetDFER · FERV39KWAR: 45.97% -> 47.67%Helped
Use 3D instances with bag size 4 instead of feeding the whole video as one instance (ablation)M3DFEL · DFEWWAR: 68.04% -> 69.25%Helped
Set bag size to 2 rather than 4 (ablation)M3DFEL · DFEWWAR: 69.25% -> 68.55%Hurt
Set bag size to 8 rather than 4 (ablation)M3DFEL · DFEWWAR: 69.25% -> 68.24%Hurt
Set bag size to 16 rather than 4 (ablation)M3DFEL · DFEWWAR: 69.25% -> 66.36%Hurt
Add DLIAM instead of average pooling (ablation)M3DFEL · DFEWWAR: 68.23 -> 69.25Helped
Add BiLSTM to DLIAM (ablation)M3DFEL · DFEWWAR: 68.63 -> 69.25Helped
Add DMIN (ablation)M3DFEL · DFEWWAR: 68.91 -> 69.25Helped
Add MHSA to DLIAM (ablation)M3DFEL · DFEWWAR: 69.13 -> 69.25Helped
Add MHSA to DLIAM (ablation)M3DFEL · DFEWUAR: 56.21 -> 56.10Hurt

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