UEyes: Understanding Visual Saliency across User Interface Types
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2023-04-19
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Abstract
While user interfaces (UIs) display elements such as images and text in a grid-based layout, UI types differ significantly in the number of elements and how they are displayed. For example, webpage designs rely heavily on images and text, whereas desktop UIs tend to feature numerous small images. To examine how such differences affect the way users look at UIs, we collected and analyzed a large eye-tracking-based dataset, UEyes (62 participants and 1,980 UI screenshots), covering four major UI types: webpage, desktop UI, mobile UI, and poster. We analyze its differences in biases related to such factors as color, location, and gaze direction. We also compare state-of-the-art predictive models and propose improvements for better capturing typical tendencies across UI types. Both the dataset and the models are publicly available.Description
Funding Information: This work was supported by Aalto University’s Department of Information and Communications Engineering, the Finnish Center for Artifcial Intelligence (FCAI), the Academy of Finland through the projects Human Automata (grant 328813) and BAD (grant 318559), the Horizon 2020 FET program of the European Union (grant CHISTERA-20-BCI-001), and the European Innovation Council Pathfnder program (SYMBIOTIK project, grant 101071147). We appreciate Chuhan Jiao’s initial implementation of the baseline methods for saliency prediction and active discussion with Yao (Marc) Wang. Publisher Copyright: © 2023 Owner/Author.
Keywords
Computer Vision, Deep Learning, Eye Tracking, Human Perception and Cognition, Interaction Design
Other note
Citation
Jiang, Y, Leiva, L A, R. B. Houssel, P, Rezazadegan Tavakoli, H, Kylmälä, J & Oulasvirta, A 2023, UEyes : Understanding Visual Saliency across User Interface Types . in CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems ., 285, ACM, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Hamburg, Germany, 23/04/2023 . https://doi.org/10.1145/3544548.3581096