MaskRNN: Instance Level Video Object Segmentation
Yuan-Ting Hu, Jia-Bin Huang, Alexander Schwing
Abstract
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.
BibTeX
@inproceedings{NIPS2017_6c9882bb,
author = {Hu, Yuan-Ting and Huang, Jia-Bin and Schwing, Alexander},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {MaskRNN: Instance Level Video Object Segmentation},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/6c9882bbac1c7093bd25041881277658-Paper.pdf},
volume = {30},
year = {2017}
}