@inproceedings{491156f521254349b1f56a63b4408e09,
title = "An accurate saliency prediction method based on generative adversarial networks",
abstract = "In this paper, we propose a saliency prediction algorithm utilizing generative adversarial networks. The proposed system contains two parts: saliency network and adversarial networks. The saliency network is the basis for saliency prediction, which calculates an Euclidean cost function on the grayscale values between the predicted saliency map and the ground truth. In order to improve the accuracy of the algorithm, adversarial networks are subsequently utilized to extract the features of input data by coordinating the learning rates of the two sub-networks contained in the networks. Experimental results validate the high accuracy of the proposed approach compared with the state-of-the-art models on three public datasets, SALICON, MIT1003 and Cerf.",
keywords = "Accuracy, Adversarial networks, GAN, Saliency network, Saliency prediction",
author = "Bing Yan and Haoqian Wang and Xingzheng Wang and Yongbing Zhang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICIP.2017.8296700",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2339--2343",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
address = "美国",
}