  In this work, we describe our method for tackling the valence-arousal
estimation challenge from ABAW2 ICCV-2021 Competition. The competition
organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze
affective behavior in real-life settings. We use a two stream model to learn
emotion features from appearance and action respectively. To solve data
imbalanced problem, we apply label distribution smoothing (LDS) to re-weight
labels. Our proposed method achieves Concordance Correlation Coefficient (CCC)
of 0.591 and 0.617 for valence and arousal on the validation set of Aff-wild2
dataset.
