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AN ACCURATE SALIENCY PREDICTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORKS

Citation Author(s):
Bing Yan,Haoqian Wang,Xingzheng Wang,Yongbing Zhang
Submitted by:
Haoqian Wang
Last updated:
6 September 2017 - 10:53pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Bing Yan
Paper Code:
ICIP-1096
 

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.

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