EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning

TL;DR

EyeFormer is a novel model that combines Transformer-based policy networks with reinforcement learning to predict personalized eye scanpaths, including fixation points and durations, for individual users across different stimuli.

Contribution

It introduces a Transformer-guided reinforcement learning approach for personalized scanpath prediction, filling a gap in individual-level eye movement modeling.

Findings

Predicts full scanpaths including fixation positions and durations.

Capable of personalized predictions with few user samples.

Applications in GUI layout optimization.

Abstract

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.