  Data-driven saliency has recently gained a lot of attention thanks to the use
of Convolutional Neural Networks for predicting gaze fixations. In this paper
we go beyond standard approaches to saliency prediction, in which gaze maps are
computed with a feed-forward network, and present a novel model which can
predict accurate saliency maps by incorporating neural attentive mechanisms.
The core of our solution is a Convolutional LSTM that focuses on the most
salient regions of the input image to iteratively refine the predicted saliency
map. Additionally, to tackle the center bias typical of human eye fixations,
our model can learn a set of prior maps generated with Gaussian functions. We
show, through an extensive evaluation, that the proposed architecture
outperforms the current state of the art on public saliency prediction
datasets. We further study the contribution of each key component to
demonstrate their robustness on different scenarios.
