Multi-modal Multi-label Facial Action Unit Detection with Transformer
원문 초록
Facial Action Coding System is an important approach of facial expression analysis.This paper describes our submission to the third Affective Behavior Analysis (ABAW) 2022 competition. We proposed a transfomer based model to detect facial action unit (FAU) in video. To be specific, we firstly trained a multi-modal model to extract both audio and visual feature. After that, we proposed a action units correlation module to learn relationships between each action unit labels and refine action unit detection result. Experimental results on validation dataset shows that our method achieves better performance than baseline model, which verifies that the effectiveness of proposed network.
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Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample
Learning Contrastive Feature Representations for Facial Action Unit Detection
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention
Multitask Emotion Recognition with Incomplete Labels
토론
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