MaskRNN: Instance Level Video Object Segmentation

Yuan-Ting Hu (UIUC), Jia-Bin Huang (Virginia Tech), Alexander G. Schwing (UIUC)


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.



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Poster [Download]

  author = {Hu, Yuan-Ting and Huang, Jia-Bin and Schwing, Alexander G.},
  title = {{MaskRNN: Instance Level Video Object Segmentation}},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2017},


Precomputed segmentation masks: DAVIS-2016 DAVIS-2017