  Annotating videos with object segmentation masks typically involves a two
stage procedure of drawing polygons per object instance for all the frames and
then linking them through time. While simple, this is a very tedious, time
consuming and expensive process, making the creation of accurate annotations at
scale only possible for well-funded labs. What if we were able to segment an
object in the full video with only a single click? This will enable video
segmentation at scale with a very low budget opening the door to many
applications. Towards this goal, in this paper we propose a bottom up approach
where given a single click for each object in a video, we obtain the
segmentation masks of these objects in the full video. In particular, we
construct a correlation volume that assigns each pixel in a target frame to
either one of the objects in the reference frame or the background. We then
refine this correlation volume via a recurrent attention module and decode the
final segmentation. To evaluate the performance, we label the popular and
challenging Cityscapes dataset with video object segmentations. Results on this
new CityscapesVideo dataset show that our approach outperforms all the
baselines in this challenging setting.
