# Understanding Visual Saliency in Mobile User Interfaces

arXiv: https://arxiv.org/abs/2101.09176 · HTML: https://mlsanity.com/paper/2101.09176/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 69 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Learning Visual Importance for Graphic Designs and Data Visualizations](https://mlsanity.com/paper/1708.02660/) (2017)
- [Understanding and Visualizing Deep Visual Saliency Models](https://mlsanity.com/paper/1903.02501/) (2019)
- [Saliency Revisited: Analysis of Mouse Movements versus Fixations](https://mlsanity.com/paper/1705.10546/) (2017)
- [Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challenges](https://mlsanity.com/paper/1810.03716/) (2018)
- [Saliency Prediction for Mobile User Interfaces](https://mlsanity.com/paper/1711.03726/) (2017)

## Built on by (most cited first, 13 of 13)

- [UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset](https://mlsanity.com/paper/2407.08850/) (2024)
- [Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight](https://mlsanity.com/paper/2509.10723/) (2025)
- [An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages](https://mlsanity.com/paper/2206.08604/) (2022)
- [Trends, Applications, and Challenges in Human Attention Modelling](https://mlsanity.com/paper/2402.18673/) (2024)
- [EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning](https://mlsanity.com/paper/2404.10163/) (2024)
- [UniAR: A Unified model for predicting human Attention and Responses on visual content](https://mlsanity.com/paper/2312.10175/) (2023)
- [Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction](https://mlsanity.com/paper/2404.14232/) (2024)
- [Scanpath Prediction on Information Visualisations](https://mlsanity.com/paper/2112.02340/) (2021)
- [How do Visual Attributes Influence Web Agents? A Comprehensive Evaluation of User Interface Design Factors](https://mlsanity.com/paper/2601.21961/) (2026)
- [Predicting Visual Attention in Graphic Design Documents](https://mlsanity.com/paper/2407.02439/) (2024)
- [Impact of Design Decisions in Scanpath Modeling](https://mlsanity.com/paper/2405.08981/) (2024)
- [Clustered Saliency Prediction](https://mlsanity.com/paper/2207.02205/) (2022)
- [A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces](https://mlsanity.com/paper/2503.17212/) (2025)

## 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.