  In this paper, we propose the solution to the Multi-Task Learning (MTL)
Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW)
competition. The task of ABAW is to predict frame-level emotion descriptors
from videos: discrete emotional state; valence and arousal; and action units.
Although researchers have proposed several approaches and achieved promising
results in ABAW, current works in this task rarely consider interactions
between different emotion descriptors. To this end, we propose a novel end to
end architecture to achieve full integration of different types of information.
Experimental results demonstrate the effectiveness of our proposed solution.
