Citation¶
If you use RankSEG in your research, please consider citing our work. We appreciate your support! ⭐
Papers¶
RankSEG is based on the following research papers:
Foundational Paper (JMLR 2023)
The core RankSEG framework and methodology:
@article{rankseg2023,
title={RankSEG: A Consistent Ranking-based Framework for Segmentation},
author={Dai, Ben and Li, Chunlin},
journal={Journal of Machine Learning Research},
volume={24},
number={224},
pages={1--50},
year={2023}
}
Paper Link: https://www.jmlr.org/papers/v24/22-0712.html
Extended Work (NeurIPS 2025)
The RMA solver for efficient segmentation:
@inproceedings{rankseg2025,
title={RankSEG-RMA: An Efficient Segmentation Algorithm via Reciprocal Moment Approximation},
author={Wang, Zixun and Dai, Ben},
booktitle={Advances in Neural Information Processing Systems},
year={2025}
}
Paper Link: https://openreview.net/forum?id=4tRMm1JJhw
License¶
RankSEG is released under the BSD 3-Clause License. See the LICENSE file for details.