  This paper proposes a framework for the interactive video object segmentation
(VOS) in the wild where users can choose some frames for annotations
iteratively. Then, based on the user annotations, a segmentation algorithm
refines the masks. The previous interactive VOS paradigm selects the frame with
some worst evaluation metric, and the ground truth is required for calculating
the evaluation metric, which is impractical in the testing phase. In contrast,
in this paper, we advocate that the frame with the worst evaluation metric may
not be exactly the most valuable frame that leads to the most performance
improvement across the video. Thus, we formulate the frame selection problem in
the interactive VOS as a Markov Decision Process, where an agent is learned to
recommend the frame under a deep reinforcement learning framework. The learned
agent can automatically determine the most valuable frame, making the
interactive setting more practical in the wild. Experimental results on the
public datasets show the effectiveness of our learned agent without any changes
to the underlying VOS algorithms. Our data, code, and models are available at
https://github.com/svip-lab/IVOS-W.
