# Predicting Eye Gaze Location on Websites

arXiv: https://arxiv.org/abs/2211.08074 · HTML: https://mlsanity.com/paper/2211.08074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Attentional Feature Fusion](https://mlsanity.com/paper/2009.14082/) (2020)
- [Contextual Encoder-Decoder Network for Visual Saliency Prediction](https://mlsanity.com/paper/1902.06634/) (2019)
- [End-to-End Saliency Mapping via Probability Distribution Prediction](https://mlsanity.com/paper/1804.01793/) (2018)
- [Visual Saliency Based on Scale-Space Analysis in the Frequency Domain](https://mlsanity.com/paper/1605.01999/) (2016)
- [Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset](https://mlsanity.com/paper/2104.12345/) (2021)
- [User Interaction Analysis through Contrasting Websites Experience](https://mlsanity.com/paper/2201.03638/) (2022)

## Built on by (most cited first, 2 of 2)

- [Deep self-supervised learning with visualisation for automatic gesture recognition](https://mlsanity.com/paper/2406.12440/) (2024)
- [Multi-Modal interpretable automatic video captioning](https://mlsanity.com/paper/2411.06872/) (2024)

## Abstract (arXiv)

World-wide-web, with the website and webpage as the main interface, facilitates the dissemination of important information. Hence it is crucial to optimize them for better user interaction, which is primarily done by analyzing users' behavior, especially users' eye-gaze locations. However, gathering these data is still considered to be labor and time intensive. In this work, we enable the development of automatic eye-gaze estimations given a website screenshots as the input. This is done by the curation of a unified dataset that consists of website screenshots, eye-gaze heatmap and website's layout information in the form of image and text masks. Our pre-processed dataset allows us to propose an effective deep learning-based model that leverages both image and text spatial location, which is combined through attention mechanism for effective eye-gaze prediction. In our experiment, we show the benefit of careful fine-tuning using our unified dataset to improve the accuracy of eye-gaze predictions. We further observe the capability of our model to focus on the targeted areas (images and text) to achieve high accuracy. Finally, the comparison with other alternatives shows the state-of-the-art result of our model establishing the benchmark for the eye-gaze prediction task.