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Previous video salient object detection (VSOD) approaches have mainly focusedon designing fancy networks to achieve their performance improvements. However,with the slow-down in development of deep learning techniques recently, it maybecome more and more difficult to anticipate another breakthrough via fancynetworks solely. To this end, this paper proposes a universal learning schemeto get a further 3\% performance improvement for all state-of-the-art (SOTA)methods. The major highlight of our method is that we resort the "motionquality"---a brand new concept, to select a sub-group of video frames from theoriginal testing set to construct a new training set. The selected frames inthis new training set should all contain high-quality motions, in which thesalient objects will have large probability to be successfully detected by the"target SOTA method"---the one we want to improve. Consequently, we can achievea significant performance improvement by using this new training set to start anew round of network training. During this new round training, the VSOD resultsof the target SOTA method will be applied as the pseudo training objectives.Our novel learning scheme is simple yet effective, and its semi-supervisedmethodology may have large potential to inspire the VSOD community in thefuture.

A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception