  Inspired by recent advances of deep learning in instance segmentation and
object tracking, we introduce video object segmentation problem as a concept of
guided instance segmentation. Our model proceeds on a per-frame basis, guided
by the output of the previous frame towards the object of interest in the next
frame. We demonstrate that highly accurate object segmentation in videos can be
enabled by using a convnet trained with static images only. The key ingredient
of our approach is a combination of offline and online learning strategies,
where the former serves to produce a refined mask from the previous frame
estimate and the latter allows to capture the appearance of the specific object
instance. Our method can handle different types of input annotations: bounding
boxes and segments, as well as incorporate multiple annotated frames, making
the system suitable for diverse applications. We obtain competitive results on
three different datasets, independently from the type of input annotation.
