Automatic affective recognition has been an important research topic in human
computer interaction (HCI) area. With recent development of deep learning
techniques and large scale in-the-wild annotated datasets, the facial emotion
analysis is now aimed at challenges in the real world settings. In this paper,
we introduce our submission to the 2nd Affective Behavior Analysis in-the-wild
(ABAW2) Competition. In dealing with different emotion representations,
including Categorical Emotions (CE), Action Units (AU), and Valence Arousal
(VA), we propose a multi-task streaming network by a heuristic that the three
representations are intrinsically associated with each other. Besides, we
leverage an advanced facial expression embedding as prior knowledge, which is
capable of capturing identity-invariant expression features while preserving
the expression similarities, to aid the down-streaming recognition tasks. The
extensive quantitative evaluations as well as ablation studies on the Aff-Wild2
dataset prove the effectiveness of our proposed prior aided streaming network
approach.

该研究提出了一种利用多任务流网络和先进面部表情嵌入的方法，在面对不同的情感表示时，有效地进行情感自动识别。