  This paper presents a new deep neural network design for salient object
detection by maximizing the integration of local and global image context
within, around, and beyond the salient objects. Our key idea is to adaptively
propagate and aggregate the image context features with variable attenuation
over the entire feature maps. To achieve this, we design the spatial
attenuation context (SAC) module to recurrently translate and aggregate the
context features independently with different attenuation factors and then to
attentively learn the weights to adaptively integrate the aggregated context
features. By further embedding the module to process individual layers in a
deep network, namely SAC-Net, we can train the network end-to-end and optimize
the context features for detecting salient objects. Compared with 29
state-of-the-art methods, experimental results show that our method performs
favorably over all the others on six common benchmark data, both quantitatively
and visually.
