  Salient object detection, 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 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 structural loss function to learn 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.
