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Authors: Ciheng Zhang 1 ; Decky Aspandi 2 and Steffen Staab 2 ; 3

Affiliations: 1 Institute of Industrial Automation and Software Engineering, University of Stuttgart, Stuttgart, Germany ; 2 Institute for Parallel and Distributed Systems, University of Stuttgart, Stuttgart, Germany ; 3 Web and Internet Science, University of Southampton, Southampton, U.K.

Keyword(s): Eye-Gaze Saliency, Image Translation, Visual Attention.

Abstract: World-Wide-Web, with website and webpage as a main interface, facilitates dissemination of important information. Hence it is crucial to optimize webpage design for better user interaction, which is primarily done by analyzing users’ behavior, especially users’ eye-gaze locations on the webpage. However, gathering these data is still considered to be labor and time intensive. In this work, we enable the development of automatic eye-gaze estimations given webpage screenshots as input by curating of a unified dataset that consists of webpage screenshots, eye-gaze heatmap and website’s layout information in the form of image and text masks. Our curated dataset allows us to propose a deep learning-based model that leverages on both webpage screenshot and content information (image and text spatial location), which are then combined through attention mechanism for effective eye-gaze prediction. In our experiment, we show benefits of careful fine-tuning using our unified dataset to improve accuracy of eye-gaze predictions. We further observe the capability of our model to focus on targeted areas (images and text) to achieve accurate eye-gaze area predictions. Finally, comparison with other alternatives shows state-of-the-art result of our approach, establishing a benchmark for webpage based eye-gaze prediction task. (More)

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Paper citation in several formats:
Zhang, C.; Aspandi, D. and Staab, S. (2023). Predicting Eye Gaze Location on Websites. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 121-132. DOI: 10.5220/0011747300003417

@conference{visapp23,
author={Ciheng Zhang. and Decky Aspandi. and Steffen Staab.},
title={Predicting Eye Gaze Location on Websites},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={121-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011747300003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Predicting Eye Gaze Location on Websites
SN - 978-989-758-634-7
IS - 2184-4321
AU - Zhang, C.
AU - Aspandi, D.
AU - Staab, S.
PY - 2023
SP - 121
EP - 132
DO - 10.5220/0011747300003417
PB - SciTePress