  Instance level video object segmentation is an important technique for video
editing and compression. To capture the temporal coherence, in this paper, we
develop MaskRNN, a recurrent neural net approach which fuses in each frame the
output of two deep nets for each object instance -- a binary segmentation net
providing a mask and a localization net providing a bounding box. Due to the
recurrent component and the localization component, our method is able to take
advantage of long-term temporal structures of the video data as well as
rejecting outliers. We validate the proposed algorithm on three challenging
benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the
Segtrack v2 dataset, achieving state-of-the-art performance on all of them.
