  Salient object detection has increasingly become a popular topic in cognitive
and computational sciences, including computer vision and artificial
intelligence research. In this paper, we propose integrating \textit{semantic
priors} into the salient object detection process. Our algorithm consists of
three basic steps. Firstly, the explicit saliency map is obtained based on the
semantic segmentation refined by the explicit saliency priors learned from the
data. Next, the implicit saliency map is computed based on a trained model
which maps the implicit saliency priors embedded into regional features with
the saliency values. Finally, the explicit semantic map and the implicit map
are adaptively fused to form a pixel-accurate saliency map which uniformly
covers the objects of interest. We further evaluate the proposed framework on
two challenging datasets, namely, ECSSD and HKUIS. The extensive experimental
results demonstrate that our method outperforms other state-of-the-art methods.
