Technical Report for Valence-Arousal Estimation in ABAW2 Challenge

• URL: http://arxiv.org/abs/2107.03891v1
• Date: Thu, 8 Jul 2021 15:21:38 GMT
• Title: Technical Report for Valence-Arousal Estimation in ABAW2 Challenge
• Authors: Hong-Xia Xie, I-Hsuan Li, Ling Lo, Hong-Han Shuai, and Wen-Huang Cheng
• Abstract summary: We tackle 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.
• Score: 20.90072006477564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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