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https://github.com/ZhanYang-nwpu/RSVG-pytorch
RSVG: Exploring Data and Model for Visual Grounding on Remote Sensing Data, 2022
https://github.com/ZhanYang-nwpu/RSVG-pytorch
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RSVG: Exploring Data and Model for Visual Grounding on Remote Sensing Data, 2022
- Host: GitHub
- URL: https://github.com/ZhanYang-nwpu/RSVG-pytorch
- Owner: ZhanYang-nwpu
- Created: 2022-09-11T09:19:20.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-12T11:31:14.000Z (10 months ago)
- Last Synced: 2024-04-12T08:41:33.670Z (6 months ago)
- Language: Python
- Size: 3.81 MB
- Stars: 85
- Watchers: 3
- Forks: 3
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
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README
# RSVG: Exploring Data and Model for Visual Grounding on Remote Sensing Data
##### Author: Yang Zhan, Zhitong Xiong, Yuan Yuan
This is the offical dataset for paper **"RSVG: Exploring Data and Model for Visual Grounding on Remote Sensing Data"**, [Paper](https://ieeexplore.ieee.org/document/10056343).
**School of Artificial Intelligence, OPtics, and ElectroNics (iOPEN), Northwestern Polytechnical University**
## Please share a STAR ⭐ if this project does help
## 📢 News
Release the DIOR_RSVG dataset.
**[2022/10/22]**: Publish the manuscript on arXiv.
## 💬 Introduction
This is Multi-Granularity Visual Language Fusion (MGVLF) Network, the PyTorch source code of the paper "RSVG: Exploring Data and Model for Visual Grounding on Remote Sensing Data". It is built on top of the [TransVG](https://github.com/djiajunustc/TransVG) in PyTorch. Our method is a transformer-based method for visual grounding for remote sensing data (RSVG). It has achieved the SOTA performance in the RSVG task on our constructed RSVG dataset.
### 📦DIOR-RSVG Dataset
### 📦Statistics of the Visual Grounding Dataset
| **Dataset** | **train** | **val** | **test** | **Overall** |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------:|:------------:|:------------:|:------------:|
| Flickr30k [[Paper]](https://arxiv.org/abs/1505.04870) [[Code]](https://github.com/BryanPlummer/pl-clc) [[Website]](http://web.engr.illinois.edu/~bplumme2/Flickr30kEntities/) | 29783 (94%) | 1000 (3%) | 1000 (3%) | 31783 |
| ReferItGame [[Paper]](http://www.aclweb.org/anthology/D14-1086) [[Website]](http://tamaraberg.com/referitgame/) | 54127 (45%) | 5842 (5%) | 60103 (50%) | 120072 |
| RefCOCO [[Paper]](https://arxiv.org/pdf/1608.00272.pdf)[[Code]](https://github.com/lichengunc/refer) | 120624 (85%) | 10834 (7%) | 5657 (3%) | 142210 |
| RefCOCO+ [[Paper]](https://arxiv.org/pdf/1608.00272.pdf)[[Code]](https://github.com/lichengunc/refer) | 120191 (85%) | 10758 (7%) | 5726 (4%) | 141564 |
| GuessWhat [[Paper]](https://arxiv.org/abs/1611.08481) [[Code]](https://github.com/GuessWhatGame/guesswhat/) [[Website]](https://guesswhat.ai/#) | 70% | 15% | 15% | 100% |
| Cops-Ref [[Paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Cops-Ref_A_New_Dataset_and_Task_on_Compositional_Referring_Expression_CVPR_2020_paper.pdf) [[Code]](https://github.com/zfchenUnique/Cops-Ref) | 119603 (80.5%) | 16524 (11%) | 12586 (8.5%) | 148713 |
| KB-Ref [[Paper]](https://arxiv.org/pdf/2006.01629) [[Code]](https://github.com/wangpengnorman/KB-Ref_dataset) | 31284 (72%) | 4000 (10%) | 8000 (18%) | 43284 |
| Ref-Reasoning [[Paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Graph-Structured_Referring_Expression_Reasoning_in_the_Wild_CVPR_2020_paper.pdf) [[Code]](https://github.com/sibeiyang/sgmn) [[Website]](https://sibeiyang.github.io/dataset/ref-reasoning/) | 721164 (91%) | 36183 (4.6%) | 34609 (4.4%) | 791956 |
| RSVG [[Paper]](https://dl.acm.org/doi/abs/10.1145/3503161.3548316) [[Website]](https://sunyuxi.github.io/publication/GeoVG) | 5505 (70%) | 1201 (15%) | 1227 (15%) | 7933 |
| **DIOR-RSVG** [[Paper]](https://ieeexplore.ieee.org/document/10056343) [[Dataset]](https://drive.google.com/drive/folders/1hTqtYsC6B-m4ED2ewx5oKuYZV13EoJp_) | **26991 (70%)** | **3829 (10%)** | **7500 (20%)** | **38320** |
### 🚀Network Architecture
## 👁️Requirements and Installation
We recommended the following dependencies.
- Python 3.6.13
- PyTorch 1.9.0
- NumPy 1.19.2
- cuda 11.1
- opencv 4.5.5
- torchvision
## 🔍Download Dataset
Download our constructed RSVG dataset files. We build the first large-scale dataset for RSVG, termed DIOR-RSVG, which can be downloaded from our [Google Drive](https://drive.google.com/drive/folders/1hTqtYsC6B-m4ED2ewx5oKuYZV13EoJp_?usp=sharing). The download link is available below:
```
https://drive.google.com/drive/folders/1hTqtYsC6B-m4ED2ewx5oKuYZV13EoJp_?usp=sharing
```
We expect the directory and file structure to be the following:
```
./ # current (project) directory
├── data_loader.py # Load data
├── main.py # Main code for training, validation, and test
├── README.md
└── DIOR_RSVG/ # DIOR-RSVG dataset
├── Annotations/ # Query expressions and bounding boxes
│ ├── 00001.xml/
│ └── ..some xml files..
├── JPEGImages/ # Remote sensing images
│ ├── 00001.jpg/
│ └── ..some jpg files..
├── train.txt # ID of training set （26991）
├── val.txt # ID of validation set （3829）
└── test.txt # ID of test set （7500）
```
## 📜Reference
If you found this code useful, please cite the paper. Welcome :+1:_`Fork and Star`_:+1:, then I will let you know when we update.
```
@ARTICLE{10056343,
author={Zhan, Yang and Xiong, Zhitong and Yuan, Yuan},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data},
year={2023},
volume={61},
number={},
pages={1-13},
doi={10.1109/TGRS.2023.3250471}
}
```
## 🙏Acknowledgments
Our DIOR-RSVG is constructed based on the [DIOR](http://www.escience.cn/people/JunweiHan/DIOR.html) remote sensing image dataset. We thank to the authors for releasing the dataset. Part of our code is borrowed from [TransVG](https://github.com/djiajunustc/TransVG). We thank to the authors for releasing codes. I would like to thank Xiong zhitong and Yuan yuan for helping the manuscript. I also thank the School of Artificial Intelligence, OPtics, and ElectroNics (iOPEN), Northwestern Polytechnical University for supporting this work.