task_categories:visual-question-answering size_categories:100K<n<1M language:en region:us
---
task_categories:
- visual-question-answering
language:
- en
pretty_name: Vision-Flan
size_categories:
- 100K<n<1M
---

# 🚀 Vision-Flan Dataset

vision-flan_191-task-1k is a human-labeled visual instruction tuning dataset consisting of 191 diverse tasks and 1,000 examples for each task.
It is constructed for visual instruction tuning and for building large-scale vision-language models.

## Paper or blog for more information:

https://github.com/VT-NLP/MultiInstruct/

https://vision-flan.github.io/

*Paper coming soon* 😊

## Citation

*Paper coming soon* 😊. If you use Vision-Flan, please use the following cites: 
```
@misc{visionFlan2023,
        title = {Vision-Flan:Scaling Visual Instruction Tuning},
        url = {https://vision-flan.github.io/},
        author = {Zhiyang Xu and Trevor Ashby and Chao Feng and Rulin Shao and Ying Shen and Di Jin and Qifan Wang and Lifu Huang},
        month = {Sep},
        year = {2023}
    }
```

```
@inproceedings{DBLP:conf/acl/XuSH23,
        author = {Zhiyang Xu and Ying Shen and Lifu Huang},
        editor = {Anna Rogers and Jordan L. Boyd{-}Graber and Naoaki Okazaki},
        title = {MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning},
        booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2023, Toronto, Canada, July 9-14, 2023},
        pages = {11445--11465},
        publisher = {Association for Computational Linguistics},
        year = {2023},
        url = {https://doi.org/10.18653/v1/2023.acl-long.641},
        doi = {10.18653/v1/2023.acl-long.641},
        timestamp = {Thu, 10 Aug 2023 12:35:59 +0200},
        biburl = {https://dblp.org/rec/conf/acl/XuSH23.bib},
        bibsource = {dblp computer science bibliography, https://dblp.org}
    }
```

## License:

Please carefully check the licenses for all the datasets on this [page](https://vision-flan.github.io/tasks.html) before use.

## Contact:
If you have any questions or concerns please contact us at zhiyangx@vt.edu .