  Semi-supervised video object segmentation is an interesting yet challenging
task in machine learning. In this work, we conduct a series of refinements with
the propagation-based video object segmentation method and empirically evaluate
their impact on the final model performance through ablation study. By taking
all the refinements, we improve the space-time memory networks to achieve a
Overall of 79.1 on the Youtube-VOS Challenge 2019.
