  Fully convolutional networks (FCN) has significantly improved the performance
of many pixel-labeling tasks, such as semantic segmentation and depth
estimation. However, it still remains non-trivial to thoroughly utilize the
multi-level convolutional feature maps and boundary information for salient
object detection. In this paper, we propose a novel FCN framework to integrate
multi-level convolutional features recurrently with the guidance of object
boundary information. First, a deep convolutional network is used to extract
multi-level feature maps and separately aggregate them into multiple
resolutions, which can be used to generate coarse saliency maps. Meanwhile,
another boundary information extraction branch is proposed to generate boundary
features. Finally, an attention-based feature fusion module is designed to fuse
boundary information into salient regions to achieve accurate boundary
inference and semantic enhancement. The final saliency maps are the combination
of the predicted boundary maps and integrated saliency maps, which are more
closer to the ground truths. Experiments and analysis on four large-scale
benchmarks verify that our framework achieves new state-of-the-art results.
