@article{Zhu_etal18sym,
  author = {Zhu, D. and Dai, L. and Luo, Y. and Zhang, G. and Shao, X. and Itti, L. and Lu, J.},
  title = {Multi-Scale Adversarial Feature Learning for Saliency Detection},
  journal = {Symmetry},
  volume = {10},
  year = {2018},
  month = {Oct},
  number = {10},
  article-number = {457},
  pages = {1-10},
  url = {http://www.mdpi.com/2073-8994/10/10/457},
  issn = {2073-8994},
  doi = {10.3390/sym10100457},
  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.},
  file = {http://ilab.usc.edu/publications/doc/Zhu_etal18sym.pdf},
  type = {mod;cv},
  if = {2018 impact factor: 1.256},
}