  Video instance segmentation (VIS) aims to segment and associate all instances
of predefined classes for each frame in videos. Prior methods usually obtain
segmentation for a frame or clip first, and then merge the incomplete results
by tracking or matching. These methods may cause error accumulation in the
merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to
generate complete sequences for input videos by a single step. We further build
a sequence propagation head on the existing image-level instance segmentation
network for long-term propagation. To ensure robustness and high recall of our
proposed framework, multiple sequences are proposed where redundant sequences
of the same instance are reduced. We achieve state-of-the-art performance on
two representative benchmark datasets -- we obtain 47.6% in terms of AP on
YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set.
