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

2022-03-24 12:23:07
Liyu Meng, Yuchen Liu, Xiaolong Liu, Zhaopei Huang, Wenqiang Jiang, Tenggan Zhang, Yuanyuan Deng, Ruichen Li, Yannan Wu, Jinming Zhao, Fengsheng Qiao, Qin Jin, Chuanhe Liu
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2203.13032

PDF

https://arxiv.org/pdf/2203.13032.pdf

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