  This paper studies the problem of semi-supervised video object
segmentation(VOS). Multiple works have shown that memory-based approaches can
be effective for video object segmentation. They are mostly based on
pixel-level matching, both spatially and temporally. The main shortcoming of
memory-based approaches is that they do not take into account the sequential
order among frames and do not exploit object-level knowledge from the target.
To address this limitation, we propose to Learn position and target Consistency
framework for Memory-based video object segmentation, termed as LCM. It applies
the memory mechanism to retrieve pixels globally, and meanwhile learns position
consistency for more reliable segmentation. The learned location response
promotes a better discrimination between target and distractors. Besides, LCM
introduces an object-level relationship from the target to maintain target
consistency, making LCM more robust to error drifting. Experiments show that
our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS
benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS
task.
