  We propose a new method for video object segmentation (VOS) that addresses
object pattern learning from unlabeled videos, unlike most existing methods
which rely heavily on extensive annotated data. We introduce a unified
unsupervised/weakly supervised learning framework, called MuG, that
comprehensively captures intrinsic properties of VOS at multiple granularities.
Our approach can help advance understanding of visual patterns in VOS and
significantly reduce annotation burden. With a carefully-designed architecture
and strong representation learning ability, our learned model can be applied to
diverse VOS settings, including object-level zero-shot VOS, instance-level
zero-shot VOS, and one-shot VOS. Experiments demonstrate promising performance
in these settings, as well as the potential of MuG in leveraging unlabeled data
to further improve the segmentation accuracy.
