| --- |
| license: apache-2.0 |
| --- |
| # Citation |
| If you find it useful, please consider citing: |
| ``` |
| @article{wang2026vision, |
| title = {Vision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Images}, |
| author = {Wang, Shanwen and Sun, Xin and Hong, Danfeng and Zhou, Fei}, |
| journal = {arXiv preprint arXiv:2602.00202}, |
| year = {2026}, |
| month = feb, |
| note = {Available at \url{https://arxiv.org/abs/2602.00202}} |
| } |
| ``` |
| # Acknowledgments |
| We sincerely thank the authors of [Qwen-VL](https://github.com/QwenLM/Qwen3-VL) and [UniMatch](https://github.com/LiheYoung/UniMatch) for their excellent open‑source work, and we also thank the contributors of the publicly available [LoveDA](https://zenodo.org/records/5706578) and [Potsdam](https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx?utm_source=chatgpt.com) datasets. Please follow the licenses and terms of the original models and datasets. |
