  Recent progress on saliency detection is substantial, benefiting mostly from
the explosive development of Convolutional Neural Networks (CNNs). Semantic
segmentation and saliency detection algorithms developed lately have been
mostly based on Fully Convolutional Neural Networks (FCNs). There is still a
large room for improvement over the generic FCN models that do not explicitly
deal with the scale-space problem. Holistically-Nested Edge Detector (HED)
provides a skip-layer structure with deep supervision for edge and boundary
detection, but the performance gain of HED on salience detection is not
obvious. In this paper, we propose a new method for saliency detection by
introducing short connections to the skip-layer structures within the HED
architecture. Our framework provides rich multi-scale feature maps at each
layer, a property that is critically needed to perform segment detection. Our
method produces state-of-the-art results on 5 widely tested salient object
detection benchmarks, with advantages in terms of efficiency (0.15 seconds per
image), effectiveness, and simplicity over the existing algorithms.
