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A large body of previous models to predict where people look in natural scenes focused on pixel-level image attributes. To bridge the semantic gap between the predictive power of computational saliency models and human behavior, we propose a new saliency architecture that incorporates information at three layers: pixel-level image attributes, object-level attributes, and semantic-level attributes. Object- and semantic-level information is frequently ignored, or only a few sample object categories are discussed where scaling to a large number of object categories is not feasible nor neurally plausible. To address this problem, this work constructs a principled vocabulary of basic attributes to describe object- and semantic-level information thus not restricting to a limited number of object categories. We build a new dataset of 700 images with eye-tracking data of 15 viewers and annotation data of 5,551 segmented objects with fine contours and 12 semantic attributes (publicly available with the paper). Experimental results demonstrate the importance of the object- and semantic-level information in the prediction of visual attention.

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Summary The social difficulties that are a hallmark of autism spectrum disorder (ASD) are thought to arise, at least in part, from atypical attention towards stimuli and their features. To investigate this hypothesis comprehensively, we characterized 700 complex natural scene images with a novel 3-layered saliency model that incorporated pixel-level (e.g., contrast), object-level (e.g., shape), and semantic-level attributes (e.g., faces) on 5551 annotated objects. Compared to matched controls, people with ASD had a stronger image center bias regardless of object distribution, reduced saliency for faces and for locations indicated by social gaze, yet a general increase in pixel-level saliency at the expense of semantic-level saliency. These results were further corroborated by direct analysis of fixation characteristics and investigation of feature interactions. Our results for the first time quantify atypical visual attention in ASD across multiple levels and categories of objects.