  Video Object Segmentation, and video processing in general, has been
historically dominated by methods that rely on the temporal consistency and
redundancy in consecutive video frames. When the temporal smoothness is
suddenly broken, such as when an object is occluded, or some frames are missing
in a sequence, the result of these methods can deteriorate significantly or
they may not even produce any result at all. This paper explores the orthogonal
approach of processing each frame independently, i.e disregarding the temporal
information. In particular, it tackles the task of semi-supervised video object
segmentation: the separation of an object from the background in a video, given
its mask in the first frame. We present Semantic One-Shot Video Object
Segmentation (OSVOS-S), based on a fully-convolutional neural network
architecture that is able to successively transfer generic semantic
information, learned on ImageNet, to the task of foreground segmentation, and
finally to learning the appearance of a single annotated object of the test
sequence (hence one shot). We show that instance level semantic information,
when combined effectively, can dramatically improve the results of our previous
method, OSVOS. We perform experiments on two recent video segmentation
databases, which show that OSVOS-S is both the fastest and most accurate method
in the state of the art.
