  In this work, we address the task of semi-supervised video object
segmentation(VOS) and explore how to make efficient use of video property to
tackle the challenge of semi-supervision. We propose a novel pipeline called
State-Aware Tracker(SAT), which can produce accurate segmentation results with
real-time speed. For higher efficiency, SAT takes advantage of the inter-frame
consistency and deals with each target object as a tracklet. For more stable
and robust performance over video sequences, SAT gets awareness for each state
and makes self-adaptation via two feedback loops. One loop assists SAT in
generating more stable tracklets. The other loop helps to construct a more
robust and holistic target representation. SAT achieves a promising result of
72.3% J&F mean with 39 FPS on DAVIS2017-Val dataset, which shows a decent
trade-off between efficiency and accuracy. Code will be released at
github.com/MegviiDetection/video_analyst.
