  Unsupervised video object segmentation aims to segment a target object in the
video without a ground truth mask in the initial frame. This challenging task
requires extracting features for the most salient common objects within a video
sequence. This difficulty can be solved by using motion information such as
optical flow, but using only the information between adjacent frames results in
poor connectivity between distant frames and poor performance. To solve this
problem, we propose a novel prototype memory network architecture. The proposed
model effectively extracts the RGB and motion information by extracting
superpixel-based component prototypes from the input RGB images and optical
flow maps. In addition, the model scores the usefulness of the component
prototypes in each frame based on a self-learning algorithm and adaptively
stores the most useful prototypes in memory and discards obsolete prototypes.
We use the prototypes in the memory bank to predict the next query frames mask,
which enhances the association between distant frames to help with accurate
mask prediction. Our method is evaluated on three datasets, achieving
state-of-the-art performance. We prove the effectiveness of the proposed model
with various ablation studies.
