  Conventional video segmentation methods often rely on temporal continuity to
propagate masks. Such an assumption suffers from issues like drifting and
inability to handle large displacement. To overcome these issues, we formulate
an effective mechanism to prevent the target from being lost via adaptive
object re-identification. Specifically, our Video Object Segmentation with
Re-identification (VS-ReID) model includes a mask propagation module and a ReID
module. The former module produces an initial probability map by flow warping
while the latter module retrieves missing instances by adaptive matching. With
these two modules iteratively applied, our VS-ReID records a global mean
(Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017
DAVIS Challenge.
