  Deep convolutional neural network significantly boosted the capability of
salient object detection in handling large variations of scenes and object
appearances. However, convolution operations seek to generate strong responses
on individual pixels, while lack the ability to maintain the spatial structure
of objects. Moreover, the down-sampling operations, such as pooling and
striding, lose spatial details of the salient objects. In this paper, we
propose a simple yet effective Siamese Edge-Enhancement Network (SE2Net) to
preserve the edge structure for salient object detection. Specifically, a novel
multi-stage siamese network is built to aggregate the low-level and high-level
features, and parallelly estimate the salient maps of edges and regions. As a
result, the predicted regions become more accurate by enhancing the responses
at edges, and the predicted edges become more semantic by suppressing the false
positives in background. After the refined salient maps of edges and regions
are produced by the SE2Net, an edge-guided inference algorithm is designed to
further improve the resulting salient masks along the predicted edges.
Extensive experiments on several benchmark datasets have been conducted, which
show that our method is superior than the state-of-the-art approaches.
