The 4th ABAW Competition: Multi-Task-Learning and Learning from Synthetic Data
ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges
This paper describes the fourth affective behavior analysis in-the-wild(abaw) competition, held in conjunction with the 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 2017conferences, and aims at automatically analyzing affect.This paper describes the fourth affective behavior analysis in-the-wild(abaw) competition, held in conjunction with the 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 2017conferences, and aims at automatically analyzing affect.The 4th abaw 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.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.
