ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges
Dimitrios Kollias
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:
this https URL