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| license: apache-2.0 |
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| # Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection |
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| [](https://arxiv.org/pdf/2506.10601) |
| [](https://github.com/antxinyuan/ssp) |
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| 🔥 We appreciate the attention to our paper. The code is available at [Github repo](https://github.com/antxinyuan/ssp). |
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| > Production from Institute of Computing Technology, Chinese Academy of Sciences. |
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| > Primary contact: Xinyuan Liu ( liuxinyuan21s@ict.ac.cn ). |
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| ## TL;DR |
| This repository contains the source code of [**Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection**](https://arxiv.org/pdf/2506.10601). |
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| 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. |
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| ### Pseudo-label performance |
| All pseudo-labeling results are available in [pseudo_labels](https://huggingface.co/antxinyuan/SSP/blob/main/pseudo_labels.zip). |
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| | 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/ | |
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| ### Detectors performance |
| | Dataset | Config | Log | Checkpoint | mAP(paper) | mAP(reproduced) | |
| | :-: | :-: | :-: | :-: | :-: | :-: | |
| | SSP(RFOCS) | [config](https://github.com/antxinyuan/ssp/blob/main/configs/ssp/rfcos_ssp_dotav10.py) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/logs/rfcos_ssp_dotav10.json) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/models/rfcos_ssp_dotav10-4c17ff33.pth) | 45.78 | 45.82 | |
| | SSP(ORCNN) | [config](https://github.com/antxinyuan/ssp/blob/main/configs/ssp/orcnn_ssp_dotav10.py) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/logs/orcnn_ssp_dotav10.json) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/models/orcnn_ssp_dotav10-2df034d3.pth) | 47.86 | 48.81 | |
| | SSP(ReDet) | [config](https://github.com/antxinyuan/ssp/blob/main/configs/ssp/orcnn_ssp_dotav20.py) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/logs/redet_ssp_dotav10.json) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/models/redet_ssp_dotav10-eed2738e.pth) | 48.50 | 49.02 | |
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| ## 🖊️ Citation |
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| If you find this work helpful for your research, please consider giving this repo a star ⭐ and citing our papers: |
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| ## Citation |
| If this work is helpful for your research, please consider citing the following BibTeX entry. |
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| ``` 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} |
| } |
| |
| ``` |
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| ## Related resources |
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| We acknowledge all the open-source contributors for the following projects to make this work possible: |
| - [PointOBB-v2](https://github.com/VisionXLab/PointOBB-v2) |
| - [MMRotate](https://github.com/open-mmlab/mmrotate) |
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