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2022

ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges

Kollias, Dimitrios

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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.

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