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. Code is available at https://github.com/marcellacornia/mlnet.
Cornia et al. REF proposed an architecture that combines features extracted at different levels of a DCNN.
A deep multi-level network for saliency prediction
2016 23rd International Conference on Pattern Recognition (ICPR)
2016 23rd International Conference on Pattern Recognition (ICPR)
Computer Science