We evaluate our PReMVOS algorithm (Proposal-generation, Refinement and Merging for Video Object Segmentation) on the new YouTube-VOS dataset for the task of semi-supervised video object segmentation (VOS). This task consists of automatically generating accurate and consistent pixel masks for multiple objects in a video sequence, given the object’s first-frame ground truth annotations. The new YouTube-VOS dataset and the corresponding challenge, the 1st Large-scale Video Object Segmentation Challenge, provide a much larger scale evaluation than any previous VOS benchmarks. Our method achieves the best results in the 2018 Large-scale Video Object Segmentation Challenge with a J&F overall mean score over both known and unknown categories of 72.2.