Awesome

<font size='5'>RSGPT: A Remote Sensing Vision Language Model and Benchmark</font>

Yuan Hu, Jianlong Yuan, Congcong Wen, Xiaonan Lu, Xiang Li☨

☨corresponding author

<a href='https://arxiv.org/abs/2307.15266'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>

This is an ongoing project. We are working on increasing the dataset size.

Related Projects

<font size='5'>VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding</font>

Xiang Li, Jian Ding, Mohamed Elhoseiny

<a href='https://vrsbench.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2406.12384'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/datasets/xiang709/VRSBench'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'>

<font size='5'>Vision-language models in remote sensing: Current progress and future trends</font>

Xiang Li☨, Congcong Wen, Yuan Hu, Zhenghang Yuan, Xiao Xiang Zhu

<a href='[https://arxiv.org/abs/2307.15266](https://ieeexplore.ieee.org/abstract/document/10506064/)'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>

:fire: Updates

• [2024.12.18] We release the manual scoring results for RSIEval.
• [2024.06.19] We release the VRSBench, A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. VRSBench contains 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. check VRSBench Project Page.
• [2024.05.23] We release the RSICap dataset. Please fill out this form to get both RSICap and RSIEval dataset.
• [2023.11.10] Our survey about vision-language models in remote sensing. RSVLM.
• [2023.10.22] The RSICap dataset and code will be released upon paper acceptance.
• [2023.10.22] We release the evaluation dataset RSIEval. Please fill out this form to get both the RSIEval dataset.

Dataset

• RSICap: 2,585 image-text pairs with high-quality human-annotated captions.
• RSIEval: 100 high-quality human-annotated captions with 936 open-ended visual question-answer pairs.

Code

The idea of finetuning our vision-language model is borrowed from MiniGPT-4. Our model is based on finetuning InstructBLIP using our RSICap dataset.

Acknowledgement

• MiniGPT-4. A popular open-source vision-language model.
• InstructBLIP. The model architecture of RSGPT follows InstructBLIP. Don't forget to check out this great open-source work if you don't know it before!
• Lavis. This repository is built upon Lavis!
• Vicuna. The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source!

If you're using RSGPT in your research or applications, please cite using this BibTeX:

@article{hu2023rsgpt,
  title={RSGPT: A Remote Sensing Vision Language Model and Benchmark},
  author={Hu, Yuan and Yuan, Jianlong and Wen, Congcong and Lu, Xiaonan and Li, Xiang},
  journal={arXiv preprint arXiv:2307.15266},
  year={2023}
}