Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges

• URL: http://arxiv.org/abs/2207.09748v1
• Date: Wed, 20 Jul 2022 08:46:18 GMT
• Title: Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
• Authors: Siyang Li, Yifan Xu, Huanyu Wu, Dongrui Wu, Yingjie Yin, Jiajiong Cao, Jingting Ding
• Abstract summary: We present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild (ABAW) competition. For MTL challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of feature vectors. For LSD challenge, we propose respective methods to combat the problems of single labels, imbalanced distribution, fine-tuning limitations, and choice of model architectures.
• Score: 23.649517834303502
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild (ABAW) competition: i) Multi-Task-Learning (MTL) Challenge and ii) Learning from Synthetic Data (LSD) Challenge. For MTL challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of feature vectors. For LSD challenge, we propose respective methods to combat the problems of single labels, imbalanced distribution, fine-tuning limitations, and choice of model architectures. Experimental results on the official validation sets from the competition demonstrated that our proposed approaches outperformed baselines by a large margin. The code is available at https://github.com/sylyoung/ABAW4-HUST-ANT.

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