Human affect recognition is an essential part of natural human-computer
interaction. However, current methods are still in their infancy, especially
for in-the-wild data. In this work, we introduce our submission to the
Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a
two-stream aural-visual analysis model to recognize affective behavior from
videos. Audio and image streams are first processed separately and fed into a
convolutional neural network. Instead of applying recurrent architectures for
temporal analysis we only use temporal convolutions. Furthermore, the model is
given access to additional features extracted during face-alignment. At
training time, we exploit correlations between different emotion
representations to improve performance. Our model achieves promising results on
the challenging Aff-Wild2 database.

本文介绍了我们提交到 ABAW 2020 竞赛的两条流声音视觉分析模型来识别视频中的情感行为。该模型分别处理音频和图像流，并使用卷积神经网络进行分析，同时使用人脸对齐期间提取的附加功能。在训练期间，利用不同情感表达之间的相关性来提高性能。通过在 Aff-Wild2 数据库上的实验，我们的模型取得了有希望的结果。