  Understanding human visual attention is important for multimedia
applications. Many studies have attempted to learn from eye-tracking data and
build computational saliency prediction models. However, limited efforts have
been devoted to saliency prediction for Web pages, which are characterized by
more diverse content elements and spatial layouts. In this paper, we propose a
novel end-to-end deep generative saliency model for Web pages. To capture
position biases introduced by page layouts, a Position Prior Learning
sub-network is proposed, which models position biases as multivariate Gaussian
distribution using variational auto-encoder. To model different elements of a
Web page, a Multi Discriminative Region Detection (MDRD) branch and a Text
Region Detection(TRD) branch are introduced, which target to extract
discriminative localizations and "prominent" text regions likely to correspond
to human attention, respectively. We validate the proposed model with FiWI, a
public Web-page dataset, and shows that the proposed model outperforms the
state-of-art models for Web-page saliency prediction.
