  Salient object detection (SOD), which aims to identify and locate the most
salient pixels or regions in images, has been attracting more and more interest
due to its various real-world applications. However, this vision task is quite
challenging, especially under complex image scenes. Inspired by the intrinsic
reflection of natural images, in this paper we propose a novel feature learning
framework for large-scale salient object detection. Specifically, we design a
symmetrical fully convolutional network (SFCN) to effectively learn
complementary saliency features under the guidance of lossless feature
reflection. The location information, together with contextual and semantic
information, of salient objects are jointly utilized to supervise the proposed
network for more accurate saliency predictions. In addition, to overcome the
blurry boundary problem, we propose a new weighted structural loss function to
ensure clear object boundaries and spatially consistent saliency. The coarse
prediction results are effectively refined by these structural information for
performance improvements. Extensive experiments on seven saliency detection
datasets demonstrate that our approach achieves consistently superior
performance and outperforms the very recent state-of-the-art methods with a
large margin.
