论文标题
ABAW：从合成数据和多任务学习挑战中学习
ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges
论文作者
论文摘要
This paper describes the fourth Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with European Conference on Computer Vision (ECCV), 2022. The 4th ABAW Competition is a continuation of the Competitions held at IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Conferences, and aims at automatically analyzing affect.在本竞赛的先前跑步中，挑战针对的价值估计，表达分类和动作单位检测。今年的竞争包括两个不同的挑战：i）多任务学习的挑战，其目标是同时学习（即在多任务学习环境中）所有上述三个任务； ii）从合成数据中学习一个，即目标是学会识别人为生成的数据中的基本表达式并推广到真实数据。 AFF-WILD2数据库是一个大规模的野外数据库，也是第一个包含价和唤醒，表达式和动作单元的注释的数据库。该数据库是上述挑战的基础。更详细地：i）S-Aff-Wild2（AFF-WILD2数据库的静态版本）已被构建和利用，以实现多任务学习挑战的目的； ii）从AFF-WILD2数据库中使用的一些特定帧图像以表达操作方式使用来创建合成数据集，这是从合成数据挑战中学习的基础。在本文中，首先，我们与利用的语料库一起提出了两个挑战，然后概述了评估指标，并最终提出了每个挑战的基线系统及其衍生结果。有关比赛的更多信息，请参见竞赛的网站：https：//ibug.doc.ic.ac.ac.uk/resources/eccv-2023-4th-abaw/。
This paper describes the fourth Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with European Conference on Computer Vision (ECCV), 2022. The 4th ABAW Competition is a continuation of the Competitions held at IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Conferences, and aims at automatically analyzing affect. In the previous runs of this Competition, the Challenges targeted Valence-Arousal Estimation, Expression Classification and Action Unit Detection. This year the Competition encompasses two different Challenges: i) a Multi-Task-Learning one in which the goal is to learn at the same time (i.e., in a multi-task learning setting) all the three above mentioned tasks; and ii) a Learning from Synthetic Data one in which the goal is to learn to recognise the basic expressions from artificially generated data and generalise to real data. The Aff-Wild2 database is a large scale in-the-wild database and the first one that contains annotations for valence and arousal, expressions and action units. This database is the basis for the above Challenges. In more detail: i) s-Aff-Wild2 -- a static version of Aff-Wild2 database -- has been constructed and utilized for the purposes of the Multi-Task-Learning Challenge; and ii) some specific frames-images from the Aff-Wild2 database have been used in an expression manipulation manner for creating the synthetic dataset, which is the basis for the Learning from Synthetic Data Challenge. In this paper, at first we present the two Challenges, along with the utilized corpora, then we outline the evaluation metrics and finally present the baseline systems per Challenge, as well as their derived results. More information regarding the Competition can be found in the competition's website: https://ibug.doc.ic.ac.uk/resources/eccv-2023-4th-abaw/.
