本文概述了CVPR 2018上关于面部动作单元检测的研究，包括弱监督聚类、对抗训练、卷积神经网络滤波器优化和先验概率分类器学习的方法。研究中提到的瓶颈在于数据集的质量和规模，如AFEW、EmotioNet和BP4D。注意力机制和配置学习（如贝叶斯网、CRF）在解决AU的空间和时间定位及识别方面发挥了重要作用。CNN是当前的主要识别工具，结合注意力机制能提升性能。
List
- 1. Learning Facial Action Units From Web Images With Scalable Weakly Supervised Clustering
- 2. Weakly Supervised Facial Action Unit Recognition Through Adversarial Training
- 3. Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
- 4. Classifier Learning With Prior Probabilities for Facial Action Unit Recognition
Weakly-Supervised Attention and Relation Learning for Facial Action Unit Detection. PAMI submission.
CRF for learning relation.
Dataset
AFEW dataset: collected from Web, mostly dark, can be wearing glasses.
EmotioNet
BP4D
Thoughts
This is a field already with a lot of researches going on. The bottleneck is still the dataset. BP4D is comprehensive but sort of small-scale. The EmotioNet is large-scale yet i
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