  We address semi-supervised video object segmentation, the task of
automatically generating accurate and consistent pixel masks for objects in a
video sequence, given the first-frame ground truth annotations. Towards this
goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and
Merging for Video Object Segmentation). Our method separates this problem into
two steps, first generating a set of accurate object segmentation mask
proposals for each video frame and then selecting and merging these proposals
into accurate and temporally consistent pixel-wise object tracks over a video
sequence in a way which is designed to specifically tackle the difficult
challenges involved with segmenting multiple objects across a video sequence.
Our approach surpasses all previous state-of-the-art results on the DAVIS 2017
video object segmentation benchmark with a J & F mean score of 71.6 on the
test-dev dataset, and achieves first place in both the DAVIS 2018 Video Object
Segmentation Challenge and the YouTube-VOS 1st Large-scale Video Object
Segmentation Challenge.
