  Manually labeling video datasets for segmentation tasks is extremely time
consuming. In this paper, we introduce ScribbleBox, a novel interactive
framework for annotating object instances with masks in videos. In particular,
we split annotation into two steps: annotating objects with tracked boxes, and
labeling masks inside these tracks. We introduce automation and interaction in
both steps. Box tracks are annotated efficiently by approximating the
trajectory using a parametric curve with a small number of control points which
the annotator can interactively correct. Our approach tolerates a modest amount
of noise in the box placements, thus typically only a few clicks are needed to
annotate tracked boxes to a sufficient accuracy. Segmentation masks are
corrected via scribbles which are efficiently propagated through time. We show
significant performance gains in annotation efficiency over past work. We show
that our ScribbleBox approach reaches 88.92% J&F on DAVIS2017 with 9.14 clicks
per box track, and 4 frames of scribble annotation.
