62 research outputs found
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges
This paper describes the third Affective Behavior Analysis in-the-wild (ABAW)
Competition, held in conjunction with IEEE International Conference on Computer
Vision and Pattern Recognition (CVPR), 2022. The 3rd ABAW Competition is a
continuation of the Competitions held at ICCV 2021, IEEE FG 2020 and IEEE CVPR
2017 Conferences, and aims at automatically analyzing affect. This year the
Competition encompasses four Challenges: i) uni-task Valence-Arousal
Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit
Detection, and iv) Multi-Task-Learning. All the Challenges are based on a
common benchmark database, Aff-Wild2, which is a large scale in-the-wild
database and the first one to be annotated in terms of valence-arousal,
expressions and action units. In this paper, we present the four Challenges,
with the utilized Competition corpora, we outline the evaluation metrics and
present the baseline systems along with their obtained results
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection
This paper presents our Facial Action Units (AUs) recognition submission to
the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our
approach consists of three main modules: (i) a pre-trained facial
representation encoder which produce a strong facial representation from each
input face image in the input sequence; (ii) an AU-specific feature generator
that specifically learns a set of AU features from each facial representation;
and (iii) a spatio-temporal graph learning module that constructs a
spatio-temporal graph representation. This graph representation describes AUs
contained in all frames and predicts the occurrence of each AU based on both
the modeled spatial information within the corresponding face and the learned
temporal dynamics among frames. The experimental results show that our approach
outperformed the baseline and the spatio-temporal graph representation learning
allows our model to generate the best results among all ablated systems. Our
model ranks at the 4th place in the AU recognition track at the 5th ABAW
Competition
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