Action Unit Detection with Joint Adaptive Attention and Graph Relation
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Abstract (arXiv)
This paper describes an approach to the facial action unit (AU) detection. In this work, we present our submission to the Field Affective Behavior Analysis (ABAW) 2021 competition. The proposed method uses the pre-trained JAA model as the feature extractor, and extracts global features, face alignment features and AU local features on the basis of multi-scale features. We take the AU local features as the input of the graph convolution to further consider the correlation between AU, and finally use the fused features to classify AU. The detected accuracy was evaluated by 0.5*accuracy + 0.5*F1. Our model achieves 0.674 on the challenging Aff-Wild2 database.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 9 Jul 2021.
Builds on
9 of 15 references.
References · 9
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
- 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
- 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
- 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
- 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
- 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
- 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
- 2020JhatAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attentioncited by 62
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Citing papers per year: 2022: 1.
1 paper.