This paper introduces our approach to the EmotioNet Challenge 2020. We pose
the AU recognition problem as a multi-task learning problem, where the
non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head
motion (the last 6 AUs) are modeled separately. The co-occurrence of the
expression features and the head pose features are explored. We observe that
different AUs converge at various speed. By choosing the optimal checkpoint for
each AU, the recognition results are improved. We are able to obtain a final
score of 0.746 in validation set and 0.7306 in the test set of the challenge.

该研究论文讨论了 EmotioNet Challenge 2020 上的 AU 识别问题，并将其视为一项多任务学习问题，通过分别对非刚性面部肌肉运动（主要是前 17 个 AUs）和刚性头部运动（最后 6 个 AUs）进行建模。研究观察了表情特征和头部姿态特征的共现关系。通过选择最佳的检查点来进行 AU 识别，提高了识别结果，最终在验证集和测试集中获得了分别为 0.746 和 0.7306 的得分。

2020 TAL EmotioNet 挑战赛：多任务学习中模型选择问题的重新思考

TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in  Multi-Task Learning

This paper details the methodology and results of the EmotioNet challenge.
This challenge is the first to test the ability of computer vision algorithms
in the automatic analysis of a large number of images of facial expressions of
emotion in the wild. The challenge was divided into two tracks. The first track
tested the ability of current computer vision algorithms in the automatic
detection of action units (AUs). Specifically, we tested the detection of 11
AUs. The second track tested the algorithms' ability to recognize emotion
categories in images of facial expressions. Specifically, we tested the
recognition of 16 basic and compound emotion categories. The results of the
challenge suggest that current computer vision and machine learning algorithms
are unable to reliably solve these two tasks. The limitations of current
algorithms are more apparent when trying to recognize emotion. We also show
that current algorithms are not affected by mild resolution changes, small
occluders, gender or age, but that 3D pose is a major limiting factor on
performance. We provide an in-depth discussion of the points that need special
attention moving forward.

本文介绍了 EmotioNet 挑战的方法和结果，其中第一项挑战测试了当前计算机视觉算法在自动检测表情动作单元（AU）方面的能力，第二项挑战测试了算法在面部表情图像的情感分类方面的能力，研究表明当前机器学习算法在这两个任务中无法可靠地解决问题，计算机视觉算法在检测动作单元时的效果比检测情感分类时的效果要好。