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antxinyuan/
SSP

Name: SSP
Availability: InStock

2506.10601

apache-2.0

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Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection

arxiv

Github

🔥 We appreciate the attention to our paper. The code is available at Github repo.

Production from Institute of Computing Technology, Chinese Academy of Sciences.

Primary contact: Xinyuan Liu ( liuxinyuan21s@ict.ac.cn ).

TL;DR

This repository contains the source code of Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection.

To tackle inadequate sample assignment and instance confusion in point-supervised oriented object detection for remote sensing dense scenes, we propose SSP (Semantic-decoupled Spatial Partition), a framework integrating rule-driven prior injection and data-driven label purification. Its core innovations include pixel-level spatial partition for sample assignment and semantic-modulated box extraction for pseudo-label generation.

Pseudo-label performance

All pseudo-labeling results are available in pseudo_labels.

Dataset	mAP	mIoU	ann_file
DOTA-v1.0	34.95	49.03	pseudo_labels/ssp_dotav10_hybrid/
DOTA-v1.5	28.89	44.92	pseudo_labels/ssp_dotav15_hybrid/
DOTA-v2.0	24.72	41.93	pseudo_labels/ssp_dotav20_hybrid/

Detectors performance

Dataset	Config	Log	Checkpoint	mAP(paper)	mAP(reproduced)
SSP(RFOCS)	config	hugging face	hugging face	45.78	45.82
SSP(ORCNN)	config	hugging face	hugging face	47.86	48.81
SSP(ReDet)	config	hugging face	hugging face	48.50	49.02

🖊️ Citation

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Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

bibtex

@misc{liu2025ssp,
      title={Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection}, 
      author={Xinyuan Liu and Hang Xu and Yike Ma and Yucheng Zhang and Feng Dai},
      year={2025},
      eprint={2506.10601},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.10601}, 
}

@inproceedings{xu2024acm,
  title={Rethinking boundary discontinuity problem for oriented object detection},
  author={Xu, Hang and Liu, Xinyuan and Xu, Haonan and Ma, Yike and Zhu, Zunjie and Yan, Chenggang and Dai, Feng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17406--17415},
  year={2024}
}

Related resources

We acknowledge all the open-source contributors for the following projects to make this work possible:

antxinyuan/SSP