  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.
