  Deep learning-based video salient object detection has recently achieved
great success with its performance significantly outperforming any other
unsupervised methods. However, existing data-driven approaches heavily rely on
a large quantity of pixel-wise annotated video frames to deliver such promising
results. In this paper, we address the semi-supervised video salient object
detection task using pseudo-labels. Specifically, we present an effective video
saliency detector that consists of a spatial refinement network and a
spatiotemporal module. Based on the same refinement network and motion
information in terms of optical flow, we further propose a novel method for
generating pixel-level pseudo-labels from sparsely annotated frames. By
utilizing the generated pseudo-labels together with a part of manual
annotations, our video saliency detector learns spatial and temporal cues for
both contrast inference and coherence enhancement, thus producing accurate
saliency maps. Experimental results demonstrate that our proposed
semi-supervised method even greatly outperforms all the state-of-the-art fully
supervised methods across three public benchmarks of VOS, DAVIS, and FBMS.
