End-to-end Convolutional Network for Saliency Prediction

By Junting Pan, Xavier Giró-i-Nieto. 2015

27 citations recorded by the source corpus.

The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles. This paper however addresses the problem with a completely data-driven approach by training a convolutional network. The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep architecture which is both fast and accurate. The convolutional network in this paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction with a superior performance in all considered metrics.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jianfei Cai
2. G. Wang
3. Jiuxiang Gu
4. Amir Shahroudy
5. Tsuhan Chen
6. Bing Shuai
7. Gang Wang
8. Jason Kuen

Citations in the corpus

• Recent advances in convolutional neural networks (2015, methodology): “[173] is the only work making use of CNN to perform visual saliency prediction in an end-to-end manner, which means the CNN model accepts raw pixels as input and produces saliency map as output.”