Create README.md

README.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+- visual-question-answering
+pretty_name: VQAonline
+---
+# VQAonline
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6337e9b676421c05430a0287/6vt42q8w7EWx9vVuZqc3U.png)
+
+[**🌐 Homepage**](https://vqaonline.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/ChongyanChen/VQAonline/) | [**📖 arXiv**](https://arxiv.org/abs/2311.15562) 
+
+## Dataset Description
+We introduce VQAonline, the first VQA dataset in which all contents originate from an authentic use case. 
+
+VQAonline includes 64K visual questions sourced from an online question answering community (i.e., StackExchange).
+
+It differs from prior datasets; examples include that it contains: 
+- (1) authentic context that clarifies the question
+- (2) an answer the individual asking the question validated as acceptable from all community provided answers,
+- (3) answers that are considerably longer (e.g., a mean of 173 words versus typically 11 words or fewer in prior work)
+- (4) user-chosen topics for each visual question from 105 diverse topics revealing the dataset’s inherent diversity.
+
+## Dataset Structure
+We designed VQAonline to support few-shot settings given the recent exciting developments around in-context few-shot learning with foundation models.  
+- Training set: 665 examples
+- Validation set: 285 examples
+- Test set: 63,746 examples 
+
+
+
+## Contact
+- Chongyan Chen: [email protected]
+
+## Citation
+**BibTeX:**
+```bibtex
+@article{chen2023vqaonline,
+  title={Fully Authentic Visual Question Answering Dataset from Online Communities},
+  author={Chen, Chongyan and Liu, Mengchen and Codella, Noel and Li, Yunsheng and Yuan, Lu and Gurari, Danna},
+  journal={arXiv preprint arXiv:2311.15562},
+  year={2023}
+}
+```