UEyes: Understanding Visual Saliency across User Interface Types
Yue Jiang, Luis A. Leiva, Hamed Rezazadegan Tavakoli, Paul R. B. Houssel, Julia Kylmälä, Antti Oulasvirta
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext f04265e1-334c-4779-a9fa-8f185df57408Cited by top-tier papers10
- UICrit: Enhancing Automated Design Evaluation with a UI Critique DatasetPeitong Duan, Chin-Yi Cheng, Gang Li, Bjoern Hartmann et al.UIST 2024 · 22 citations
- EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement LearningYue Jiang, Zixin Guo, Hamed Rezazadegan Tavakoli, Luis A. Leiva et al.UIST 2024 · 15 citations
- DiffEye: Diffusion-Based Continuous Eye-Tracking Data Generation Conditioned on Natural ImagesOzgur Kara, Harris Nisar, James M. RehgNeurIPS 2025 · 7 citations
- Just-In-Time Objectives: A General Approach for Specialized AI InteractionsMichelle S. Lam, Omar Shaikh, Hallie Xu, Alice Guo et al.CHI 2026 · 3 citations
- Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath PredictionGiuseppe Cartella, Vittorio Cuculo, Alessandro D'Amelio, Marcella Cornia et al.ICCV 2025 · 3 citations
Builds on5
- DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingAkis Linardos, Matthias Kümmerer, Ori Press, Matthias BethgeICCV 2021 · 98 citations
- Screen Parsing: Towards Reverse Engineering of UI Models from ScreenshotsJason Wu, Xiaoyi Zhang, Jeffrey Nichols, Jeffrey P. BighamUIST 2021 · 62 citations
- Predicting Visual Importance Across Graphic Design TypesCamilo Fosco, Vincent Casser, Amish Kumar Bedi, Peter O'Donovan et al.UIST 2020 · 55 citations
- Relationship Between Visual Complexity and Aesthetics of WebpagesAliaksei Miniukovich, Maurizio MarcheseCHI 2020 · 24 citations
- How Much Time Do You Have? Modeling Multi-Duration SaliencyCamilo Fosco, Anelise Newman, Pat Sukhum, Yun Bin Zhang et al.CVPR 2020
Related papers
- WebUI: A Dataset for Enhancing Visual UI Understanding with Web SemanticsJason Wu, Siyan Wang, Siman Shen, Yi-Hao Peng et al.CHI 2023 · 49 citations
- Owl Eyes: Spotting UI Display Issues via Visual UnderstandingZhe Liu, Chunyang Chen, Junjie Wang, Yuekai Huang et al.ASE 2020 · 79 citations
- HiFiGaze: Improving Eye Tracking Accuracy Using Screen Content KnowledgeTaejun Kim, Vimal Mollyn, Riku Arakawa, Chris HarrisonCHI 2026 · 2 citations
- UI-Lens: Assessing General MLLMs' Potential to Automate UI Display Quality AssuranceWei Xiang, Yexinrui Wu, Xinli Chen, Xinran Li et al.CVPR 2026
- Stretch Gaze Targets Out: Experimenting with Target Sizes for Gaze-Enabled Interfaces on Mobile DevicesOmar Namnakani, Yasmeen Abdrabou, Jonathan Grizou, Mohamed KhamisCHI 2025 · 4 citations
