Predicting Visual Attention in Graphic Design Documents

Souradeep Chakraborty

doi:10.1109/TMM.2022.3176942

2023-01-01

Souradeep Chakraborty *

Zijun Wei

Seoyoung Ahn

Aruna Balasubramanian

Gregory J. Zelinsky

Dimitris Samaras

DOI：10.1109/TMM.2022.3176942

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摘要

En 中文

We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency of graphic designs, our work is the first attempt to predict both spatial attention and dynamic temporal order in which the document regions are fixated by gaze using a deep learning based model. We propose a two-stage model for predicting dynamic attention on such documents, with webpages being our primary choice of document design for demonstration. In the first stage, we predict the saliency maps for each of the document components (e.g. logos, banners, texts, etc. for webpages) conditioned on the type of document layout. These component saliency maps are then jointly used to predict the overall document saliency. In the second stage, we use these layout-specific component saliency maps as the state representation for an inverse reinforcement learning model of fixation scanpath prediction during document viewing. To test our model, we collected a new dataset consisting of eye movements from 41 people freely viewing 450 webpages (the largest dataset of its kind). Experimental results show that our model outperforms existing models in both saliency and scanpath prediction for webpages, and also generalizes very well to other graphic design documents such as comics, posters, mobile UIs, etc. and natural images.

Keyword:

Predictive models

Graphics

Layout

Computational modeling

Visualization

Deep learning

Faces

Graphic design

webpage

mobile UI

segmen- tation