  Recent deep learning-based video salient object detection (VSOD) has achieved
some breakthrough, but these methods rely on expensive annotated videos with
pixel-wise annotations, weak annotations, or part of the pixel-wise
annotations. In this paper, based on the similarities and the differences
between VSOD and image salient object detection (SOD), we propose a novel VSOD
method via a progressive framework that locates and segments salient objects in
sequence without utilizing any video annotation. To use the knowledge learned
in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to
compensate for the lack of motion information of SOD during the locating
process but retain the same fine segmenting process. Specifically, an algorithm
for generating spatiotemporal location labels, which consists of generating
high-saliency location labels and tracking salient objects in adjacent frames,
is proposed. Based on these location labels, a two-stream locating network that
introduces an optical flow branch for video salient object locating is
presented. Although our method does not require labeled video at all, the
experimental results on five public benchmarks of DAVIS, FBMS, ViSal, VOS, and
DAVSOD demonstrate that our proposed method is competitive with fully
supervised methods and outperforms the state-of-the-art weakly and unsupervised
methods.
