Action Unit Detection with Joint Adaptive Attention and Graph Relation
arXiv:2107.04389
Abstract
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.
References in corpus (6)
- Semi-Supervised Classification with Graph Convolutional Networks
- Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
- Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
- Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study
- Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework
- Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace