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A benchmark of saliency models performance with a synthetic image dataset isprovided. Model performance is evaluated through saliency metrics as well asthe influence of model inspiration and consistency with human psychophysics.SID4VAM is composed of 230 synthetic images, with known salient regions. Imageswere generated with 15 distinct types of low-level features (e.g. orientation,brightness, color, size...) with a target-distractor pop-out type of syntheticpatterns. We have used Free-Viewing and Visual Search task instructions and 7feature contrasts for each feature category. Our study reveals thatstate-of-the-art Deep Learning saliency models do not perform well withsynthetic pattern images, instead, models with Spectral/Fourier inspirationoutperform others in saliency metrics and are more consistent with humanpsychophysical experimentation. This study proposes a new way to evaluatesaliency models in the forthcoming literature, accounting for synthetic imageswith uniquely low-level feature contexts, distinct from previous eye trackingimage datasets.

SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling