Atypical Salient Regions Enhancement Network for visual saliency prediction of individuals with Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder and can result in atypical visual perception towards stimuli. Existing atypical saliency prediction models for individuals with ASD are highly inspired by typical saliency prediction models, while neglecting their special visual traits and preferences which are different from neurotypical individuals. In this paper, we propose an Atypical Salient Regions Enhancement Network (ASD-ASRENet) based on an encoder–decoder architecture to predict the atypical visual saliency of individuals with ASD. Concretely, the output of the encoder is treated as the initial prediction, then four Atypical Salient Regions Enhancement (ASRE) modules, which are specially designed for individuals with ASD, are deployed at different stages of the decoder to emphasize the atypical salient regions and further complete the prediction in a progressive manner. Besides, considering the problem that the semantic information from high levels is gradually diluted while the effect of noises contained in low levels is increasingly stronger during the top-down transmission in the decoder, we further design a Global Semantics Flow (GSF) module, which captures the global interdependencies from both spatial perspective and channel perspective, to guide each integration stage in the decoder. Extensive experiments demonstrate the effectiveness of the proposed modules and our ASD-ASRENet achieves superior performance compared with all state-of-the-art models on the Saliency4ASD benchmark.