  Visual saliency detection aims at identifying the most visually distinctive
parts in an image, and serves as a pre-processing step for a variety of
computer vision and image processing tasks. To this end, the saliency detection
procedure must be as fast and compact as possible and optimally processes input
images in a real time manner. It is an essential application requirement for
the saliency detection task. However, contemporary detection methods often
utilize some complicated procedures to pursue feeble improvements on the
detection precession, which always take hundreds of milliseconds and make them
not easy to be applied practically. In this paper, we tackle this problem by
proposing a fast and compact saliency score regression network which employs
fully convolutional network, a special deep convolutional neural network, to
estimate the saliency of objects in images. It is an extremely simplified
end-to-end deep neural network without any pre-processings and
post-processings. When given an image, the network can directly predict a dense
full-resolution saliency map (image-to-image prediction). It works like a
compact pipeline which effectively simplifies the detection procedure. Our
method is evaluated on six public datasets, and experimental results show that
it can achieve comparable or better precision performance than the
state-of-the-art methods while get a significant improvement in detection speed
(35 FPS, processing in real time).
