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Poster
MaskRNN: Instance Level Video Object Segmentation
Yuan-Ting Hu · Jia-Bin Huang · Alex Schwing

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #84
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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.

Keywords: Computer Vision · Video, Motion and Tracking · Image Segmentation
[ Paper] 

Author Information

Yuan-Ting Hu (University of Illinois Urbana-Champaign)
Jia-Bin Huang (Virginia Tech)
Alex Schwing (University of Illinois at Urbana-Champaign)

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