The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the
first Competition aiming at automatic analysis of the three main behavior tasks
of valence-arousal estimation, basic expression recognition and action unit
detection. It is split into three Challenges, each one addressing a respective
behavior task. For the Challenges, we provide a common benchmark database,
Aff-Wild2, which is a large scale in-the-wild database and the first one
annotated for all these three tasks. In this paper, we describe this
Competition, to be held in conjunction with the IEEE Conference on Face and
Gesture Recognition, May 2020, in Buenos Aires, Argentina. We present the three
Challenges, with the utilized Competition corpora. We outline the evaluation
metrics, present both the baseline system and the top-3 performing teams'
methodologies per Challenge and finally present their obtained results. More
information regarding the Competition, the leaderboard of each Challenge and
details for accessing the utilized database, are provided in the Competition
site:
this http URL

这篇论文介绍了 Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition，它是第一个旨在自动分析情感行为的比赛，分为三个挑战，分别是估计情感价值 - 唤醒、基本表情识别和动作单元检测。其中，Aff-Wild2 是一个大规模的真实环境数据库，是第一个为所有这三项任务注释的数据库。

分析第一届 ABAW 2020 竞赛中的情感行为

Analysing Affective Behavior in the First ABAW 2020 Competition

Empathy, as defined in behavioral sciences, expresses the ability of human
beings to recognize, understand and react to emotions, attitudes and beliefs of
others. The lack of an operational definition of empathy makes it difficult to
measure it. In this paper, we address two related problems in automatic
affective behavior analysis: the design of the annotation protocol and the
automatic recognition of empathy from spoken conversations. We propose and
evaluate an annotation scheme for empathy inspired by the modal model of
emotions. The annotation scheme was evaluated on a corpus of real-life, dyadic
spoken conversations. In the context of behavioral analysis, we designed an
automatic segmentation and classification system for empathy. Given the
different speech and language levels of representation where empathy may be
communicated, we investigated features derived from the lexical and acoustic
spaces. The feature development process was designed to support both the fusion
and automatic selection of relevant features from high dimensional space. The
automatic classification system was evaluated on call center conversations
where it showed significantly better performance than the baseline.

本文提出并评估了一种情感识别标注方案，通过构建自动分割和分类系统，通过调查语言和声学空间中的特征来识别通话中的共情。该系统在呼叫中心对话中表现出明显的优异性能。