EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
Authors: 
Yue Jiang, Zixin Guo, Hamed Rezazadegan Tavakoli, 
Luis A. Leiva, 
Antti OulasvirtaAuthors Info & Claims
Yue Jiang, Zixin Guo, Hamed Rezazadegan Tavakoli, Luis A. Leiva, Antti OulasvirtaAuthors Info & ClaimsArticle No.: 47, Pages 1 - 15
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”, no scanpath model has been capable of predicting scanpaths for an individual. To close this gap, we introduce EyeFormer, which utilizes a Transformer architecture as a policy network to guide a deep reinforcement learning algorithm that predicts gaze locations. Our model offers the unique capability of producing personalized predictions when given a few user scanpath samples. It can predict full scanpath information, including fixation positions and durations, across individuals and various stimulus types. Additionally, we demonstrate applications in GUI layout optimization driven by our model.
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Supplementary materials for the paper 'EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning'. This document provides additional details on the implementation of the dataset, algorithm, and training process. Furthermore, we present an expanded set of experimental results and analyses of population-level and individual-level scanpath predictions. The video is a preview for the paper, offering a general overview of its contents.
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The document provides additional details on the implementation of the dataset, algorithm, and training process reported upon in ``EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning.'' In addition, it presents a more comprehensive set of experimental results and analysis of population-level and individual-level scanpath predictions. The video is a video preview for the paper.
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The document provides additional details on the implementation of the dataset, algorithm, and training process reported upon in ``EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning.'' In addition, it presents a more comprehensive set of experimental results and analysis of population-level and individual-level scanpath predictions. The video is a video preview for the paper.
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Supplementary materials for the paper 'EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning'. This document provides additional details on the implementation of the dataset, algorithm, and training process. Furthermore, we present an expanded set of experimental results and analyses of population-level and individual-level scanpath predictions. The video is a preview for the paper, offering a general overview of its contents.
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- 59.79 MB
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- EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
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- the Horizon 2020 FET program of the European Union
- the Academy of Finland BAD
- the Academy of Finland through the projects Human Automata
- the European Innovation Council Pathfinder program SYMBIOTIK project
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UIST '24: The 37th Annual ACM Symposium on User Interface Software and Technology
October 13 - 16, 2024
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