Learning to Predict where the Children with Asd Look

Abstract

As is known to us, people with Autism Spectrum Disorder (ASD) have atypical visual attention towards stimuli. Learning the visual attention of people especially, children, with ASD contribute to related research in the field of medicine and psychology. In this paper, we first construct a saliency prediction for children with autism (SPCA) database, which is the first of its kind and consists of 500 images and the corresponding eye tracking data collected from 13 different children with ASD. We compare the performance of five state-of-the-art deep neural networks (DNN)-based saliency prediction approaches with their original networks and the fine-tuned networks on our database. We predict the atypical visual attention of children with ASD for the first time and get the best saliency prediction results for individuals with ASD so far.