EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided
  Reinforcement Learning
Yue Jiang Zixin Guo Hamed Rezazadegan Tavakoli Luis A. Leiva Antti Oulasvirta
From a visual perception perspective, modern graphical user interfaces (GUIs)
comprise a complex graphics-rich two-dimensional visuospatial arrangement of
text, images, and interactive objects such as buttons and menus. While existing
models can accurately predict regions and objects that are likely to attract
attention ``on average'', so far there is no scanpath model capable of
predicting scanpaths for an individual. To close this gap, we introduce
EyeFormer, which leverages a Transformer architecture as a policy network to
guide a deep reinforcement learning algorithm that controls gaze locations. Our
model has the unique capability of producing personalized predictions when
given a few user scanpath samples. It can predict full scanpath information,
including fixation positions and duration, across individuals and various
stimulus types. Additionally, we demonstrate applications in GUI layout
optimization driven by our model. Our software and models will be publicly
available.