Technical Report for Valence-Arousal Estimation on Affwild2 Dataset
I-Hsuan Li
TL;DR
This paper presents a method for valence-arousal estimation using the Aff-Wild2 dataset, employing MIMAMO Net to improve emotion recognition in real-world videos, achieving CCC scores of 0.415 and 0.511.
Contribution
The work applies MIMAMO Net to the Aff-Wild2 dataset for affective computing, demonstrating its effectiveness in valence-arousal estimation in naturalistic settings.
Findings
Achieved CCC of 0.415 for valence
Achieved CCC of 0.511 for arousal
Validated on the Aff-Wild2 dataset
Abstract
In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net \cite{deng2020mimamo} model to achieve information about micro-motion and macro-motion for improving video emotion recognition and achieve Concordance Correlation Coefficient (CCC) of 0.415 and 0.511 for valence and arousal on the reselected validation set.
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Taxonomy
TopicsEmotion and Mood Recognition · Mental Health Research Topics · Human Pose and Action Recognition