  Facial expression in-the-wild is essential for various interactive computing
domains. Especially, "Emotional Reaction Intensity" (ERI) is an important topic
in the facial expression recognition task. In this paper, we propose a
multi-emotional task learning-based approach and present preliminary results
for the ERI challenge introduced in the 5th affective behavior analysis
in-the-wild (ABAW) competition. Our method achieved the mean PCC score of
0.3254.
