  The understanding of where humans look in a scene is a problem of great
interest in visual perception and computer vision. When eye-tracking devices
are not a viable option, models of human attention can be used to predict
fixations. In this paper we give two contribution. First, we show a model of
visual attention that is simply based on deep convolutional neural networks
trained for object classification tasks. A method for visualizing saliency maps
is defined which is evaluated in a saliency prediction task. Second, we
integrate the information of these maps with a bottom-up differential model of
eye-movements to simulate visual attention scanpaths. Results on saliency
prediction and scores of similarity with human scanpaths demonstrate the
effectiveness of this model.
