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Click to download PDF version Click to download BibTeX data Clik to view abstract D. Zhu, L. Dai, Y. Luo, G. Zhang, X. Shao, L. Itti, J. Lu, Multi-Scale Adversarial Feature Learning for Saliency Detection, Symmetry, Vol. 10, No. 10, pp. 1-10, Oct 2018. [2018 impact factor: 1.256] (Cited by 21)

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


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