Multi-modal Emotion Estimation for in-the-wild Videos

Multi-modal Emotion Estimation for in-the-wild Videos

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 t...

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Hauptverfasser: Meng, Liyu, Liu, Yuchen, Liu, Xiaolong, Huang, Zhaopei, Cheng, Yuan, Wang, Meng, Liu, Chuanhe, Jin, Qin
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Computer Science - Computer Vision and Pattern Recognition
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Liu, Yuchen
Liu, Xiaolong
Huang, Zhaopei
Cheng, Yuan
Wang, Meng
Liu, Chuanhe
Jin, Qin
description 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.
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