  Unsupervised video object segmentation (UVOS) is a per-pixel binary labeling
problem which aims at separating the foreground object from the background in
the video without using the ground truth (GT) mask of the foreground object.
Most of the previous UVOS models use the first frame or the entire video as a
reference frame to specify the mask of the foreground object. Our question is
why the first frame should be selected as a reference frame or why the entire
video should be used to specify the mask. We believe that we can select a
better reference frame to achieve the better UVOS performance than using only
the first frame or the entire video as a reference frame. In our paper, we
propose Easy Frame Selector (EFS). The EFS enables us to select an 'easy'
reference frame that makes the subsequent VOS become easy, thereby improving
the VOS performance. Furthermore, we propose a new framework named as Iterative
Mask Prediction (IMP). In the framework, we repeat applying EFS to the given
video and selecting an 'easier' reference frame from the video than the
previous iteration, increasing the VOS performance incrementally. The IMP
consists of EFS, Bi-directional Mask Prediction (BMP), and Temporal Information
Updating (TIU). From the proposed framework, we achieve state-of-the-art
performance in three UVOS benchmark sets: DAVIS16, FBMS, and SegTrack-V2.
