  This paper presents a novel deep architecture for saliency prediction.
Current state of the art models for saliency prediction employ Fully
Convolutional networks that perform a non-linear combination of features
extracted from the last convolutional layer to predict saliency maps. We
propose an architecture which, instead, combines features extracted at
different levels of a Convolutional Neural Network (CNN). Our model is composed
of three main blocks: a feature extraction CNN, a feature encoding network,
that weights low and high level feature maps, and a prior learning network. We
compare our solution with state of the art saliency models on two public
benchmarks datasets. Results show that our model outperforms under all
evaluation metrics on the SALICON dataset, which is currently the largest
public dataset for saliency prediction, and achieves competitive results on the
MIT300 benchmark.
