Predicting Human Attention using Computational Attention

Most models of visual attention are aimed at predicting either top-down or bottom-up control, as studied using different visual search and free-viewing tasks. We propose Human Attention Transformer (HAT), a single model predicting both forms of attention control. HAT is the new state-of-the-art (SOTA) in predicting the scanpath of fixations made during target-present and target-absent search, and matches or exceeds SOTA in the prediction of taskless free-viewing fixation scanpaths. HAT achieves this new SOTA by using a novel transformer-based architecture and a simplified foveated retina that collectively create a spatio-temporal awareness akin to the dynamic visual working memory of humans. Unlike previous methods that rely on a coarse grid of fixation cells and experience information loss due to fixation discretization, HAT features a dense-prediction architecture and outputs a dense heatmap for each fixation, thus avoiding discretizing fixations. HAT sets a new standard in computational attention, which emphasizes both effectiveness and generality. HAT's demonstrated scope and applicability will likely inspire the development of new attention models that can better predict human behavior in various attention-demanding scenarios.