Affective Behaviour Analysis via Progressive Learning
This work addresses the challenge of developing emotionally intelligent technology for applications like human-computer interaction, though it is incremental as it builds on existing competition frameworks.
The paper tackles the problem of multi-task affective behavior analysis by proposing a progressive learning framework, achieving first place in the ABAW competition with a total score of 1.5286, including specific metrics like an AU F-score of 0.5580.
Affective Behavior Analysis aims to develop emotionally intelligent technology that can recognize and respond to human emotions. To advance this field, the 7th Affective Behavior Analysis in-the-wild (ABAW) competition holds the Multi-Task Learning Challenge based on the s-Aff-Wild2 database. The participants are required to develop a framework that achieves Valence-Arousal Estimation, Expression Recognition, and AU detection simultaneously. To achieve this goal, we propose a progressive multi-task learning framework that fully leverages the distinct focuses of each task on facial emotion features. Specifically, our method design can be summarized into three main aspects: 1) Separate Training and Joint Training: We first train each task model separately and then perform joint training based on the pre-trained models, fully utilizing the feature focus aspects of each task to improve the overall framework performance. 2) Feature Fusion and Temporal Modeling:} We investigate effective strategies for fusing features extracted from each task-specific model and incorporate temporal feature modeling during the joint training phase, which further refines the performance of each task. 3) Joint Training Strategy Optimization: To identify the optimal joint training approach, we conduct a comprehensive strategy search, experimenting with various task combinations and training methodologies to further elevate the overall performance of each task. According to the official results, our team achieves first place in the MTL challenge with a total score of 1.5286 (i.e., AU F-score 0.5580, Expression F-score 0.4286, CCC VA score 0.5420). Our code is publicly available at https://github.com/YenanLiu/ABAW7th.