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

TL;DR

The paper details the 4th ABAW Competition focusing on multi-task learning and synthetic data for affect recognition, presenting datasets, evaluation metrics, baseline systems, and results to advance in-the-wild affect analysis.

Contribution

It introduces two new challenges: multi-task learning of affect tasks and learning from synthetic data, along with datasets, evaluation methods, and baseline results.

Findings

Baseline systems established for both challenges.

Synthetic data can generalize to real affect recognition.

Multi-task learning improves affect analysis performance.

Abstract

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/.