  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.

Computer Vision and Pattern Recognition