  Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with
very high prevalence around the world. Research progress in the field of ASD
facial analysis in pediatric patients has been hindered due to a lack of
well-established baselines. In this paper, we propose the use of the Vision
Transformer (ViT) for the computational analysis of pediatric ASD. The
presented model, known as ViTASD, distills knowledge from large facial
expression datasets and offers model structure transferability. Specifically,
ViTASD employs a vanilla ViT to extract features from patients' face images and
adopts a lightweight decoder with a Gaussian Process layer to enhance the
robustness for ASD analysis. Extensive experiments conducted on standard ASD
facial analysis benchmarks show that our method outperforms all of the
representative approaches in ASD facial analysis, while the ViTASD-L achieves a
new state-of-the-art. Our code and pretrained models are available at
https://github.com/IrohXu/ViTASD.
