  In this paper, we aim to tackle the task of semi-supervised video object
segmentation across a sequence of frames where only the ground-truth
segmentation of the first frame is provided. The challenges lie in how to
online update the segmentation model initialized from the first frame
adaptively and accurately, even in presence of multiple confusing instances or
large object motion. The existing approaches rely on selecting the region of
interest for model update, which however, is rough and inflexible, leading to
performance degradation. To overcome this limitation, we propose a novel
approach which utilizes reinforcement learning to select optimal adaptation
areas for each frame, based on the historical segmentation information. The RL
model learns to take optimal actions to adjust the region of interest inferred
from the previous frame for online model updating. To speed up the model
adaption, we further design a novel multi-branch tree based exploration method
to fast select the best state action pairs. Our experiments show that our work
improves the state-of-the-art of the mean region similarity on DAVIS 2016
dataset to 87.1%.
