  In this work, we propose a mask propagation network to treat the video
segmentation problem as a concept of the guided instance segmentation. Similar
to most MaskTrack based video segmentation methods, our method takes the mask
probability map of previous frame and the appearance of current frame as
inputs, and predicts the mask probability map for the current frame.
Specifically, we adopt the Xception backbone based DeepLab v3+ model as the
probability map predictor in our prediction pipeline. Besides, instead of the
full image and the original mask probability, our network takes the region of
interest of the instance, and the new mask probability which warped by the
optical flow between the previous and current frames as the inputs. We also
ensemble the modified One-Shot Video Segmentation Network to make the final
predictions in order to retrieve and segment the missing instance.
