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.

本研究提出了人类注意力变压器（HAT），它是一种单一模型，预测两种形式的注意力控制，并通过使用一种新颖的基于转换器的架构和一种简化的凹面视网膜，实现了类似于人类动态视觉工作记忆的时空意识，同时避免了离散化固定目光。HAT 在计算注意力方面设定了新方向，并在各种注意力需求场景中更好地预测人类行为，具有广泛的适用性。