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。

野外双流感性音视频分析

Two-Stream Aural-Visual Affect Analysis in the Wild

Natural human-computer interaction and audio-visual human behaviour sensing
systems, which would achieve robust performance in-the-wild are more needed
than ever as digital devices are increasingly becoming an indispensable part of
our life. Accurately annotated real-world data are the crux in devising such
systems. However, existing databases usually consider controlled settings, low
demographic variability, and a single task. In this paper, we introduce the
SEWA database of more than 2000 minutes of audio-visual data of 398 people
coming from six cultures, 50% female, and uniformly spanning the age range of
18 to 65 years old. Subjects were recorded in two different contexts: while
watching adverts and while discussing adverts in a video chat. The database
includes rich annotations of the recordings in terms of facial landmarks,
facial action units (FAU), various vocalisations, mirroring, and continuously
valued valence, arousal, liking, agreement, and prototypic examples of
(dis)liking. This database aims to be an extremely valuable resource for
researchers in affective computing and automatic human sensing and is expected
to push forward the research in human behaviour analysis, including cultural
studies. Along with the database, we provide extensive baseline experiments for
automatic FAU detection and automatic valence, arousal and (dis)liking
intensity estimation.

SEWA 数据库提供了超过 2000 分钟的语音和视觉数据，包括来自六种文化、年龄在 18 至 65 岁之间的 398 人。此外，数据库还提供了基于面部和语音特征的多项标注，如面部标记、面部行动单元（FAU）、各种音频反应等等。这是情感计算领域非常有价值的研究资源，相信会推动自然人机交互、自动人体感知、人类行为分析等研究领域的进一步发展。