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:

.. code-block:: bibtex

   @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:

.. code-block:: bibtex

   @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 <https://github.com/rankseg/rankseg/blob/main/LICENSE>`_ file for details.