  We propose a novel solution for semi-supervised video object segmentation. By
the nature of the problem, available cues (e.g. video frame(s) with object
masks) become richer with the intermediate predictions. However, the existing
methods are unable to fully exploit this rich source of information. We resolve
the issue by leveraging memory networks and learn to read relevant information
from all available sources. In our framework, the past frames with object masks
form an external memory, and the current frame as the query is segmented using
the mask information in the memory. Specifically, the query and the memory are
densely matched in the feature space, covering all the space-time pixel
locations in a feed-forward fashion. Contrast to the previous approaches, the
abundant use of the guidance information allows us to better handle the
challenges such as appearance changes and occlussions. We validate our method
on the latest benchmark sets and achieved the state-of-the-art performance
(overall score of 79.4 on Youtube-VOS val set, J of 88.7 and 79.2 on DAVIS
2016/2017 val set respectively) while having a fast runtime (0.16 second/frame
on DAVIS 2016 val set).
