  In this paper, we briefly introduce our submission to the Valence-Arousal
Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW)
competition. Our method utilizes the multi-modal information, i.e., the visual
and audio information, and employs a temporal encoder to model the temporal
context in the videos. Besides, a smooth processor is applied to get more
reasonable predictions, and a model ensemble strategy is used to improve the
performance of our proposed method. The experiment results show that our method
achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation
set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed
method.
