PReMVOS: Proposal-generation, Refinement and Merging for the DAVIS Challenge on Video Object Segmentation 2018

We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations. Towards this goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and Merging for Video Object Segmentation). This method involves generating coarse object proposals using a Mask R-CNN like object detector, followed by a refinement network that produces accurate pixel masks for each proposal. We then select and link these proposals over time using a merging algorithm that takes into account an objectness score, the optical flow warping, and a Re-ID feature embedding vector for each proposal. We adapt our networks to the target video domain by fine-tuning on a large set of augmented images generated from the first-frame ground truth. Our approach surpasses all previous state-of-the-art results on the DAVIS 2017 video object segmentation benchmark and achieves first place in the DAVIS 2018 Video Object Segmentation Challenge with a mean of J & F score of 74.7.