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

ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis