ML Anthology

Towards Good Practices for Video Object Segmentation

Dongdong Yu, Kai Su, Hengkai Guo, Jian Wang, Kaihui Zhou, Yuanyuan Huang, Minghui Dong, Jie Shao, Changhu Wang
ICCVW 2019 pp. 701-704
doi:10.1109/ICCVW.2019.00086 /iccvw/2019/yu2019iccvw-good/

Abstract

Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empirically evaluate their impact on the final model performance through ablation study. By taking all the refinements, we improve the space-time memory networks to achieve a Overall of 79.1 on the Youtube-VOS Challenge 2019.

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Cite

Text

Yu et al. "Towards Good Practices for Video Object Segmentation." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00086

Markdown

[Yu et al. "Towards Good Practices for Video Object Segmentation." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/yu2019iccvw-good/) doi:10.1109/ICCVW.2019.00086

BibTeX

@inproceedings{yu2019iccvw-good,
  title     = {{Towards Good Practices for Video Object Segmentation}},
  author    = {Yu, Dongdong and Su, Kai and Guo, Hengkai and Wang, Jian and Zhou, Kaihui and Huang, Yuanyuan and Dong, Minghui and Shao, Jie and Wang, Changhu},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {701-704},
  doi       = {10.1109/ICCVW.2019.00086},
  url       = {https://mlanthology.org/iccvw/2019/yu2019iccvw-good/}
}