Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition
This work addresses automatic affective recognition for human-computer interaction, but it is incremental as it builds on existing competition frameworks and datasets.
The paper tackled multi-task affective recognition by proposing a prior aided streaming network that leverages facial expression embeddings as prior knowledge, achieving effectiveness proven through extensive evaluations on the Aff-Wild2 dataset.
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.