Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

The paper proposes a framework for interactive video object segmentation (VOS) in the wild, where users can choose some frames for annotations iteratively.

Data and Resources

Cite this as

Zhaoyuan Yin, Jia Zheng, Weixin Luo, Shenhan Qian, Hanling Zhang, Shenghua Gao (2024). Dataset: Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild. https://doi.org/10.57702/mlifakrp

DOI retrieved: December 3, 2024

Additional Info

Field Value
Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2103.10391
Author Zhaoyuan Yin
More Authors
Jia Zheng
Weixin Luo
Shenhan Qian
Hanling Zhang
Shenghua Gao
Homepage https://github.com/svip-lab/IVOS-W