# Saliency Prediction for Mobile User Interfaces

arXiv: https://arxiv.org/abs/1711.03726 · HTML: https://mlsanity.com/paper/1711.03726/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [Understanding Visual Saliency in Mobile User Interfaces](https://mlsanity.com/paper/2101.09176/) (2021)
- [Scanpath Prediction on Information Visualisations](https://mlsanity.com/paper/2112.02340/) (2021)
- [Predicting Visual Attention in Graphic Design Documents](https://mlsanity.com/paper/2407.02439/) (2024)
- [Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights](https://mlsanity.com/paper/2405.09695/) (2024)
- [A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces](https://mlsanity.com/paper/2503.17212/) (2025)

## Abstract (arXiv)

We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons, text, etc. in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied area. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.