mlsanityData to 7 Oct 2026

Understanding Visual Saliency in Mobile User Interfaces

arXiv 2101.09176 ↗First posted 22 Jan 2021cs.HC cs.CVcited by 13

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Abstract (arXiv)

For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on desktop and web-based UIs, mobile app UIs differ from these in several respects. We present findings from a controlled study with 30 participants and 193 mobile UIs. The results speak to a role of expectations in guiding where users look at. Strong bias toward the top-left corner of the display, text, and images was evident, while bottom-up features such as color or size affected saliency less. Classic, parameter-free saliency models showed a weak fit with the data, and data-driven models improved significantly when trained specifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also release the first annotated dataset for investigating visual saliency in mobile UIs.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 9 of its 69 references that are in the map. First posted on arXiv 22 Jan 2021.

Builds on

9 of 69 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Visual Saliency Based on Multiscale Deep Featurescited by 185
  3. 2016What do different evaluation metrics tell us about saliency models?cited by 184
  4. 2016Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Modelcited by 91
  5. 2017Learning Visual Importance for Graphic Designs and Data Visualizationscited by 22
  6. 2019Understanding and Visualizing Deep Visual Saliency Modelscited by 13
  7. 2017Saliency Revisited: Analysis of Mouse Movements versus Fixationscited by 12
  8. 2018Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challengescited by 7
  9. 2017Saliency Prediction for Mobile User Interfacescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 6; 2025: 2; 2026: 1.

13 papers.

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  1. 2024UICrit: Enhancing Automated Design Evaluation with a UICritique Datasetcited by 21
  2. 2025Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversightcited by 15
  3. 2022An F-shape Click Model for Information Retrieval on Multi-block Mobile Pagescited by 10
  4. 2024Trends, Applications, and Challenges in Human Attention Modellingcited by 9
  5. 2024EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learningcited by 8
  6. 2023UniAR: A Unified model for predicting human Attention and Responses on visual contentcited by 7
  7. 2024Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Predictioncited by 6
  8. 2021Scanpath Prediction on Information Visualisationscited by 5
  9. 2026How do Visual Attributes Influence Web Agents? A Comprehensive Evaluation of User Interface Design Factorscited by 4
  10. 2024Predicting Visual Attention in Graphic Design Documentscited by 3
  11. 2024Impact of Design Decisions in Scanpath Modelingcited by 2
  12. 2022Clustered Saliency Predictioncited by 1
1 more
  1. 2025A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfacescited by 1