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--- |
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license: cc-by-sa-4.0 |
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task_categories: |
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- visual-question-answering |
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pretty_name: VQAonline |
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--- |
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# VQAonline |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6337e9b676421c05430a0287/6vt42q8w7EWx9vVuZqc3U.png" width="50%"> |
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[**🌐 Homepage**](https://vqaonline.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/ChongyanChen/VQAonline/) | [**📖 arXiv**](https://arxiv.org/abs/2311.15562) |
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## Dataset Description |
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We introduce VQAonline, the first VQA dataset in which all contents originate from an authentic use case. |
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VQAonline includes 64K visual questions sourced from an online question answering community (i.e., StackExchange). |
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It differs from prior datasets; examples include that it contains: |
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- (1) authentic context that clarifies the question |
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- (2) an answer the individual asking the question validated as acceptable from all community provided answers, |
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- (3) answers that are considerably longer (e.g., a mean of 173 words versus typically 11 words or fewer in prior work) |
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- (4) user-chosen topics for each visual question from 105 diverse topics revealing the dataset’s inherent diversity. |
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## Dataset Structure |
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In total, the VQAonline dataset contains 64,696 visual questions. |
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We designed VQAonline to support few-shot settings given the recent exciting developments around in-context few-shot learning with foundation models. Thus, we split the dataset as follows: |
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- Training set: 665 visual questions |
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- Validation set: 285 visual questions |
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- Test set: 63,746 visual questions |
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The questions, contexts, and answers are provided in the json files. |
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Due to the constraint of huggingface, we separate the image files into 7 folders (named from images1 to images7), each of which contains 10,000 image files, except for folder "images 7". |
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## Contact |
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- Chongyan Chen: chongyanchen_hci@utexas.edu |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@article{chen2023vqaonline, |
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title={Fully Authentic Visual Question Answering Dataset from Online Communities}, |
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author={Chen, Chongyan and Liu, Mengchen and Codella, Noel and Li, Yunsheng and Yuan, Lu and Gurari, Danna}, |
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journal={arXiv preprint arXiv:2311.15562}, |
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year={2023} |
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} |
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``` |
