  Affective Behavior Analysis aims to develop emotionally intelligent
technology that can recognize and respond to human emotions. To advance this,
the 7th Affective Behavior Analysis in-the-wild (ABAW) competition establishes
two tracks: i.e., the Multi-task Learning (MTL) Challenge and the Compound
Expression (CE) challenge based on Aff-Wild2 and C-EXPR-DB datasets. In this
paper, we present our methods and experimental results for the two competition
tracks. Specifically, it can be summarized in the following four aspects: 1) To
attain high-quality facial features, we train a Masked-Auto Encoder in a
self-supervised manner. 2) We devise a temporal convergence module to capture
the temporal information between video frames and explore the impact of window
size and sequence length on each sub-task. 3) To facilitate the joint
optimization of various sub-tasks, we explore the impact of sub-task joint
training and feature fusion from individual tasks on each task performance
improvement. 4) We utilize curriculum learning to transition the model from
recognizing single expressions to recognizing compound expressions, thereby
improving the accuracy of compound expression recognition. Extensive
experiments demonstrate the superiority of our designs.
