  We consider the task of semi-supervised video object segmentation (VOS). Our
approach mitigates shortcomings in previous VOS work by addressing detail
preservation and temporal consistency using visual warping. In contrast to
prior work that uses full optical flow, we introduce a new foreground-targeted
visual warping approach that learns flow fields from VOS data. We train a flow
module to capture detailed motion between frames using two weakly-supervised
losses. Our object-focused approach of warping previous foreground object masks
to their positions in the target frame enables detailed mask refinement with
fast runtimes without using extra flow supervision. It can also be integrated
directly into state-of-the-art segmentation networks. On the DAVIS17 and
YouTubeVOS benchmarks, we outperform state-of-the-art offline methods that do
not use extra data, as well as many online methods that use extra data.
Qualitatively, we also show our approach produces segmentations with high
detail and temporal consistency.
