Predicting personalized saliency map for people with autism spectrum disorder

People with autism spectrum disorder usually exhibit heterogeneous gaze patterns. Universal saliency prediction, which generates salient regions based on high fixations across all observers, is limited to analyzing the visual attention of autism spectrum disorders. To solve the problem, we propose a learning-based method named PSMANet to predict the personalized saliency map based on personal information. Collecting personal information and collecting large-scale datasets are challenging tasks for people with autism spectrum disorders, since they often suffer from deficits in social communication and interaction. The proposed approach introduces the image-similarity-measure based embedding to extract personal information and transfers the saliency distribution knowledge from universal saliency prediction to personalized saliency prediction. For evaluating our network, two popular metrics, Normalized Scanpath Salience (NSS) and Area Under Curve (AUC), are used. The experimental results show that it achieves good performance on the databases of people with autism spectrum disorder.