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VideoCutLER

VideoCutLER is a simple unsupervised video instance segmentation method (UnVIS) that uses a cut-synthesis-and-learn pipeline to generate pseudo-masks for multiple objects in an image and then train an unsupervised video instance segmentation model using these mask trajectories.

Data and Resources

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Cite this as

Xudong Wang, Ishan Misra, Ziyun Zeng, Rohit Girdhar, Trevor Darrell (2024). Dataset: VideoCutLER. https://doi.org/10.57702/36zuzsav

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It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

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Created December 16, 2024
Last update December 16, 2024
Author Xudong Wang
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Ishan Misra
Ziyun Zeng
Rohit Girdhar
Trevor Darrell
Homepage https://github.com/facebookresearch/CutLER