  Beneficial from Fully Convolutional Neural Networks (FCNs), saliency
detection methods have achieved promising results. However, it is still
challenging to learn effective features for detecting salient objects in
complicated scenarios, in which i) non-salient regions may have "salient-like"
appearance; ii) the salient objects may have different-looking regions. To
handle these complex scenarios, we propose a Feature Guide Network which
exploits the nature of low-level and high-level features to i) make foreground
and background regions more distinct and suppress the non-salient regions which
have "salient-like" appearance; ii) assign foreground label to
different-looking salient regions. Furthermore, we utilize a Multi-scale
Feature Extraction Module (MFEM) for each level of abstraction to obtain
multi-scale contextual information. Finally, we design a loss function which
outperforms the widely-used Cross-entropy loss. By adopting four different
pre-trained models as the backbone, we prove that our method is very general
with respect to the choice of the backbone model. Experiments on five
challenging datasets demonstrate that our method achieves the state-of-the-art
performance in terms of different evaluation metrics. Additionally, our
approach contains fewer parameters than the existing ones, does not need any
post-processing, and runs fast at a real-time speed of 28 FPS when processing a
480 x 480 image.
