Boosting Continuous Emotion Recognition with Self-Pretraining using
  Masked Autoencoders, Temporal Convolutional Networks, and Transformers
Weiwei Zhou Jiada Lu Chenkun Ling Weifeng Wang Shaowei Liu
Human emotion recognition holds a pivotal role in facilitating seamless
human-computer interaction. This paper delineates our methodology in tackling
the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) Classification
Challenge, and Action Unit (AU) Detection Challenge within the ambit of the 6th
Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Our
study advocates a novel approach aimed at refining continuous emotion
recognition. We achieve this by initially harnessing pre-training with Masked
Autoencoders (MAE) on facial datasets, followed by fine-tuning on the aff-wild2
dataset annotated with expression (Expr) labels. The pre-trained model serves
as an adept visual feature extractor, thereby enhancing the model's robustness.
Furthermore, we bolster the performance of continuous emotion recognition by
integrating Temporal Convolutional Network (TCN) modules and Transformer
Encoder modules into our framework.