  Objective Semi-supervised video object segmentation refers to segmenting the
object in subsequent frames given the object label in the first frame. Existing
algorithms are mostly based on the objectives of matching and propagation
strategies, which often make use of the previous frame with masking or optical
flow. This paper explores a new propagation method, uses short-term matching
modules to extract the information of the previous frame and apply it in
propagation, and proposes the network of Long-Short-Term similarity matching
for video object segmentation (LSMOVS) Method: By conducting pixel-level
matching and correlation between long-term matching module and short-term
matching module with the first frame and previous frame, global similarity map
and local similarity map are obtained, as well as feature pattern of current
frame and masking of previous frame. After two refine networks, final results
are obtained through segmentation network. Results: According to the
experiments on the two data sets DAVIS 2016 and 2017, the method of this paper
achieves favorable average of region similarity and contour accuracy without
online fine tuning, which achieves 86.5% and 77.4% in terms of single target
and multiple targets. Besides, the count of segmented frames per second reached
21. Conclusion: The short-term matching module proposed in this paper is more
conducive to extracting the information of the previous frame than only the
mask. By combining the long-term matching module with the short-term matching
module, the whole network can achieve efficient video object segmentation
without online fine tuning
