= PDF Reprint, = BibTeX entry, = Online Abstract
Zhu, D., Dai, L., Luo, Y., Zhang, G., Shao, X., Itti, L., Lu, J., Multi-Scale Adversarial Feature Learning for Saliency Detection, Symmetry, Vol. 10, No. 10, Oct 2018. (in press) [2018 impact factor: 1.256]
Abstract: Previous saliency detection methods usually focused on extracting powerful discriminative features to describe images with a complex background. Recently, the generative adversarial network (GAN) has shown a great ability in feature learning for synthesizing high quality natural images. Since the GAN shows a superior feature learning ability, we present a new multi-scale adversarial feature learning (MAFL) model for image saliency detection. In particular, we build this model, which is composed of two convolutional neural network (CNN) modules: the multi-scale G-network takes natural images as inputs and generates the corresponding synthetic saliency map, and we design a novel layer in the D-network, namely a correlation layer, which is used to determine whether one image is a synthetic saliency map or ground-truth saliency map. Quantitative and qualitative comparisons on several public datasets show the superiority of our approach.
Themes: Computational Modeling, Computer Vision
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Thu Jan 31 11:39:41 PST 2019