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The continuous improvement of human-computer interaction technology makes itpossible to compute emotions. In this paper, we introduce our submission to theCVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW).Sentiment analysis in human-computer interaction should, as far as possibleStart with multiple dimensions, fill in the single imperfect emotion channel,and finally determine the emotion tendency by fitting multiple results.Therefore, We exploited multimodal features extracted from video of differentlengths from the competition dataset, including audio, pose and images.Well-informed emotion representations drive us to propose a Attention-basedmultimodal framework for emotion estimation. Our system achieves theperformance of 0.361 on the validation dataset. The code is available at[https://github.com/xkwangcn/ABAW-5th-RT-IAI].

Mutilmodal Feature Extraction and Attention-based Fusion for Emotion Estimation in Videos