  Since the wide employment of deep learning frameworks in video salient object
detection, the accuracy of the recent approaches has made stunning progress.
These approaches mainly adopt the sequential modules, based on optical flow or
recurrent neural network (RNN), to learn robust spatiotemporal features. These
modules are effective but significantly increase the computational burden of
the corresponding deep models. In this paper, to simplify the network and
maintain the accuracy, we present a lightweight network tailored for video
salient object detection through the spatiotemporal knowledge distillation.
Specifically, in the spatial aspect, we combine a saliency guidance feature
embedding structure and spatial knowledge distillation to refine the spatial
features. In the temporal aspect, we propose a temporal knowledge distillation
strategy, which allows the network to learn the robust temporal features
through the infer-frame feature encoding and distilling information from
adjacent frames. The experiments on widely used video datasets (e.g., DAVIS,
DAVSOD, SegTrack-V2) prove that our approach achieves competitive performance.
Furthermore, without the employment of the complex sequential modules, the
proposed network can obtain high efficiency with 0.01s per frame.
