  Recent progress on salient object detection mainly aims at exploiting how to
effectively integrate convolutional side-output features in convolutional
neural networks (CNN). Based on this, most of the existing state-of-the-art
saliency detectors design complex network structures to fuse the side-output
features of the backbone feature extraction networks. However, should the
fusion strategies be more and more complex for accurate salient object
detection? In this paper, we observe that the contexts of a natural image can
be well expressed by a high-to-low self-learning of side-output convolutional
features. As we know, the contexts of an image usually refer to the global
structures, and the top layers of CNN usually learn to convey global
information. On the other hand, it is difficult for the intermediate
side-output features to express contextual information. Here, we design an
hourglass network with intermediate supervision to learn contextual features in
a high-to-low manner. The learned hierarchical contexts are aggregated to
generate the hybrid contextual expression for an input image. At last, the
hybrid contextual features can be used for accurate saliency estimation. We
extensively evaluate our method on six challenging saliency datasets, and our
simple method achieves state-of-the-art performance under various evaluation
metrics. Code will be released upon paper acceptance.
