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---
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---
# Dataset Card for The Cauldron
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6177322d37f32ecb1e2d4cdf/3q8wnTYvCWyFiCGn2q1OX.png)
## Dataset description
The Cauldron is part of the Idefics2 release.
It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.
## Load the dataset
To load the dataset, install the library `datasets` with `pip install datasets`. Then,
```
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")
```
to download and load the config `ai2d` for example.
## Data fields
An example of a sample looks as follows:
```
{
"images" = [PIL.Image]
"texts" = [
{
"user": "Question: How many actions are depicted in the diagram?\nChoices:\nA. 6.\nB. 4.\nC. 8.\nD. 7.\nAnswer with the letter.",
"assistant": "Answer: D",
"source": "TQA"
}
]
}
```
In `images`, there is a list of images, to be placed before the text.
In `texts`, there is a conversation between a user and an assistant about the images that is represented by a list of turns.
## Stats about the datasets in The Cauldron
| Dataset | # images | # Q/A pairs | # tokens |
|----------------------|----------|-------------|------------|
| *General visual question answering* |
| VQAv2 | 82,772 | 443,757 | 1,595,929 |
| COCO-QA | 46,287 | 78,736 | 286,982 |
| Visual7W | 14,366 | 69,817 | 279,268 |
| A-OKVQA | 16,539 | 17,056 | 236,492 |
| TallyQA | 98,680 | 183,986 | 738,254 |
| OK-VQA | 8,998 | 9,009 | 38,853 |
| HatefulMemes | 8,500 | 8,500 | 25,500 |
| VQA-RAD | 313 | 1,793 | 8,418 |
| Captioning |
| LNarratives | 507,444 | 507,444 | 21,328,731 |
| Screen2Words | 15,730 | 15,743 | 143,103 |
| VSR | 2,157 | 3,354 | 10,062 |
| *OCR, document understanding, text transcription* |
| RenderedText | 999,000 | 999,000 | 27,207,774 |
| DocVQA | 10,189 | 39,463 | 337,829 |
| TextCaps | 21,953 | 21,953 | 389,658 |
| TextVQA | 21,953 | 34,602 | 181,918 |
| ST-VQA | 17,247 | 23,121 | 127,846 |
| OCR-VQA | 165,746 | 801,579 | 6,073,824 |
| VisualMRC | 3,027 | 11,988 | 168,828 |
| IAM | 5,663 | 5,663 | 144,216 |
| InfoVQA | 2,118 | 10,074 | 61,048 |
| Diagram image-to-text| 300 | 300 | 22,196 |
| *Chart/figure understanding* |
| Chart2Text | 26,985 | 30,242 | 2,852,827 |
| DVQA | 200,000 | 2,325,316 | 8,346,234 |
| VisText | 7,057 | 9,969 | 1,245,485 |
| ChartQA | 18,271 | 28,299 | 185,835 |
| PlotQA | 157,070 | 20,249,479 | 8478299.278|
| FigureQA | 100,000 | 1,327,368 | 3,982,104 |
| MapQA | 37,417 | 483,416 | 6,470,485 |
| *Table understanding* |
| TabMWP | 22,729 | 23,059 | 1,948,166 |
| TAT-QA | 2,199 | 13,215 | 283,776 |
| HiTab | 2,500 | 7,782 | 351,299 |
| MultiHiertt | 7,619 | 7,830 | 267,615 |
| FinQA | 5,276 | 6,251 | 242,561 |
| WikiSQL | 74,989 | 86,202 | 9,680,673 |
| SQA | 8,514 | 34,141 | 1,894,824 |
| WTQ | 38,246 | 44,096 | 6,677,013 |
| *Reasoning, logic, maths* |
| GeomVerse | 9,303 | 9,339 | 2,489,459 |
| CLEVR-Math | 70,000 | 788,650 | 3,184,656 |
| CLEVR | 70,000 | 699,989 | 2,396,781 |
| IconQA | 27,315 | 29,859 | 112,969 |
| RAVEN | 42,000 | 42,000 | 105,081 |
| Inter-GPs | 1,451 | 2,101 | 8,404 |
| *Textbook/academic questions* |
| AI2D | 3,099 | 9,708 | 38,832 |
| TQA | 1,496 | 6,501 | 26,004 |
| ScienceQA | 4,985 | 6,218 | 24,872 |
| *Differences between 2 images* |
| NLVR2 | 50,426 | 86,373 | 259,119 |
| GSD | 70,939 | 141,869 | 4,637,229 |
| Spot the diff | 8,566 | 9,524 | 221,477 |
| *Screenshot to code* |
| WebSight | 500,000 | 500,000 | 276,743,299|
| DaTikz | 47,974 | 48,296 | 59,556,252 |
## Decontamination
The Cauldron contains only the train split of each sub-datasets.
On top of that, we removed the few examples containing an image also present in the test splits of MMMU, MathVista or MMBench.
## References to the original datasets
<details>
<summary>References to the original datasets</summary>
@misc{AI2D,
title={A Diagram Is Worth A Dozen Images},
author={Aniruddha Kembhavi and Mike Salvato and Eric Kolve and Minjoon Seo and Hannaneh Hajishirzi and Ali Farhadi},
year={2016},
eprint={1603.07396},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{A-OKVQA,
title={A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge},
author={Dustin Schwenk and Apoorv Khandelwal and Christopher Clark and Kenneth Marino and Roozbeh Mottaghi},
year={2022},
eprint={2206.01718},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{Chart2Text,
title = "Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model",
author = "Obeid, Jason and
Hoque, Enamul",
editor = "Davis, Brian and
Graham, Yvette and
Kelleher, John and
Sripada, Yaji",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.inlg-1.20",
doi = "10.18653/v1/2020.inlg-1.20",
pages = "138--147",
}
@inproceedings{ChartQA,
title = "{C}hart{QA}: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning",
author = "Masry, Ahmed and
Long, Do and
Tan, Jia Qing and
Joty, Shafiq and
Hoque, Enamul",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.177",
doi = "10.18653/v1/2022.findings-acl.177",
pages = "2263--2279",
}
@misc{CLEVR-Math,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
@misc{CLEVR,
title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning},
author={Justin Johnson and Bharath Hariharan and Laurens van der Maaten and Li Fei-Fei and C. Lawrence Zitnick and Ross Girshick},
year={2016},
eprint={1612.06890},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{CocoQA,
author = {Ren, Mengye and Kiros, Ryan and Zemel, Richard},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Exploring Models and Data for Image Question Answering},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/831c2f88a604a07ca94314b56a4921b8-Paper.pdf},
volume = {28},
year = {2015}
}
@misc{DaTikz,
title={AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ},
author={Jonas Belouadi and Anne Lauscher and Steffen Eger},
year={2024},
eprint={2310.00367},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Diagram image to text: https://huggingface.co/datasets/Kamizuru00/diagram_image_to_text by @Kamizuru00
@INPROCEEDINGS{DocVQA,
author={Mathew, Minesh and Karatzas, Dimosthenis and Jawahar, C. V.},
booktitle={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
title={DocVQA: A Dataset for VQA on Document Images},
year={2021},
volume={},
number={},
pages={2199-2208},
keywords={Visualization;Computer vision;Text analysis;Image recognition;Image analysis;Conferences;Layout},
doi={10.1109/WACV48630.2021.00225}}
@inproceedings{DVQA,
title={DVQA: Understanding Data Visualizations via Question Answering},
author={Kafle, Kushal and Cohen, Scott and Price, Brian and Kanan, Christopher},
booktitle={CVPR},
year={2018}
}
@misc{FigureQA,
title={FigureQA: An Annotated Figure Dataset for Visual Reasoning},
author={Samira Ebrahimi Kahou and Vincent Michalski and Adam Atkinson and Akos Kadar and Adam Trischler and Yoshua Bengio},
year={2018},
eprint={1710.07300},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{FinQA,
title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data",
author = "Chen, Zhiyu and
Chen, Wenhu and
Smiley, Charese and
Shah, Sameena and
Borova, Iana and
Langdon, Dylan and
Moussa, Reema and
Beane, Matt and
Huang, Ting-Hao and
Routledge, Bryan and
Wang, William Yang",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.300",
doi = "10.18653/v1/2021.emnlp-main.300",
pages = "3697--3711",
}
@misc{GeomVerse,
title={GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning},
author={Mehran Kazemi and Hamidreza Alvari and Ankit Anand and Jialin Wu and Xi Chen and Radu Soricut},
year={2023},
eprint={2312.12241},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{hatefulmeme,
author = {Kiela, Douwe and Firooz, Hamed and Mohan, Aravind and Goswami, Vedanuj and Singh, Amanpreet and Ringshia, Pratik and Testuggine, Davide},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {2611--2624},
publisher = {Curran Associates, Inc.},
title = {The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1b84c4cee2b8b3d823b30e2d604b1878-Paper.pdf},
volume = {33},
year = {2020}
}
@inproceedings{Hitab,
title = "{H}i{T}ab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation",
author = "Cheng, Zhoujun and
Dong, Haoyu and
Wang, Zhiruo and
Jia, Ran and
Guo, Jiaqi and
Gao, Yan and
Han, Shi and
Lou, Jian-Guang and
Zhang, Dongmei",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.78",
doi = "10.18653/v1/2022.acl-long.78",
pages = "1094--1110",
}
@article{IAM,
author = {Marti, Urs-Viktor and Bunke, H.},
year = {2002},
month = {11},
pages = {39-46},
title = {The IAM-database: An English sentence database for offline handwriting recognition},
volume = {5},
journal = {International Journal on Document Analysis and Recognition},
doi = {10.1007/s100320200071}
}
@inproceedings{IconQA,
title = {IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning},
author = {Lu, Pan and Qiu, Liang and Chen, Jiaqi and Xia, Tony and Zhao, Yizhou and Zhang, Wei and Yu, Zhou and Liang, Xiaodan and Zhu, Song-Chun},
booktitle = {The 35th Conference on Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks},
year = {2021}
}
@INPROCEEDINGS{InfographicVQA,
author={Mathew, Minesh and Bagal, Viraj and Tito, Rubèn and Karatzas, Dimosthenis and Valveny, Ernest and Jawahar, C. V.},
booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
title={InfographicVQA},
year={2022},
volume={},
number={},
pages={2582-2591},
keywords={Visualization;Computer vision;Computational modeling;Layout;Data visualization;Benchmark testing;Brain modeling;Document Analysis Datasets;Evaluation and Comparison of Vision Algorithms;Vision and Languages},
doi={10.1109/WACV51458.2022.00264}
}
@inproceedings{Inter-GPS,
title = {Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning},
author = {Lu, Pan and Gong, Ran and Jiang, Shibiao and Qiu, Liang and Huang, Siyuan and Liang, Xiaodan and Zhu, Song-Chun},
booktitle = {The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)},
year = {2021}
}
@misc{LocalizedNarratives,
title={Connecting Vision and Language with Localized Narratives},
author={Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari},
year={2020},
eprint={1912.03098},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{MapQA,
title={MapQA: A Dataset for Question Answering on Choropleth Maps},
author={Shuaichen Chang and David Palzer and Jialin Li and Eric Fosler-Lussier and Ningchuan Xiao},
year={2022},
eprint={2211.08545},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{MIMIC-IT-General-Scene-Difference,
title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning},
author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu},
year={2023},
eprint={2306.05425},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{Multihiertt,
title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data",
author = "Zhao, Yilun and
Li, Yunxiang and
Li, Chenying and
Zhang, Rui",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.454",
pages = "6588--6600",
}
@inproceedings{NLVR2,
title = "A Corpus for Reasoning about Natural Language Grounded in Photographs",
author = "Suhr, Alane and
Zhou, Stephanie and
Zhang, Ally and
Zhang, Iris and
Bai, Huajun and
Artzi, Yoav",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1644",
doi = "10.18653/v1/P19-1644",
pages = "6418--6428",
}
@INPROCEEDINGS{OCR-VQA,
author={Mishra, Anand and Shekhar, Shashank and Singh, Ajeet Kumar and Chakraborty, Anirban},
booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
title={OCR-VQA: Visual Question Answering by Reading Text in Images},
year={2019},
volume={},
number={},
pages={947-952},
keywords={Optical character recognition software;Visualization;Task analysis;Knowledge discovery;Text analysis;Text recognition;Character recognition;Optical Character Recognition (OCR), Visual Question Answering (VQA), Document image analysis, textVQA},
doi={10.1109/ICDAR.2019.00156}
}
@InProceedings{okvqa,
author = {Kenneth Marino and Mohammad Rastegari and Ali Farhadi and Roozbeh Mottaghi},
title = {OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019},
}
@InProceedings{PlotQA,
author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush},
title = {PlotQA: Reasoning over Scientific Plots},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}
@inproceedings{RAVEN,
title={RAVEN: A Dataset for Relational and Analogical Visual rEasoNing},
author={Zhang, Chi and Gao, Feng and Jia, Baoxiong and Zhu, Yixin and Zhu, Song-Chun},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
RenderedText: https://huggingface.co/datasets/wendlerc/RenderedText by @wendlerc
@inproceedings{Robut,
title = "{R}obu{T}: A Systematic Study of Table {QA} Robustness Against Human-Annotated Adversarial Perturbations",
author = "Zhao, Yilun and
Zhao, Chen and
Nan, Linyong and
Qi, Zhenting and
Zhang, Wenlin and
Tang, Xiangru and
Mi, Boyu and
Radev, Dragomir",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.334",
doi = "10.18653/v1/2023.acl-long.334",
pages = "6064--6081",
}
@inproceedings{SQA,
title = "Search-based Neural Structured Learning for Sequential Question Answering",
author = "Iyyer, Mohit and
Yih, Wen-tau and
Chang, Ming-Wei",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1167",
doi = "10.18653/v1/P17-1167",
pages = "1821--1831",
}
@misc{WikiSQL,
title={Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning},
author={Victor Zhong and Caiming Xiong and Richard Socher},
year={2017},
eprint={1709.00103},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{WTQ,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and
Liang, Percy",
editor = "Zong, Chengqing and
Strube, Michael",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P15-1142",
doi = "10.3115/v1/P15-1142",
pages = "1470--1480",
}
@inproceedings{ScienceQA,
author = {Lu, Pan and Mishra, Swaroop and Xia, Tanglin and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Kalyan, Ashwin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
pages = {2507--2521},
publisher = {Curran Associates, Inc.},
title = {Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Paper-Conference.pdf},
volume = {35},
year = {2022}
}
@inproceedings{screen2words,
author = {Wang, Bryan and Li, Gang and Zhou, Xin and Chen, Zhourong and Grossman, Tovi and Li, Yang},
title = {Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning},
year = {2021},
isbn = {9781450386357},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3472749.3474765},
doi = {10.1145/3472749.3474765},
booktitle = {The 34th Annual ACM Symposium on User Interface Software and Technology},
pages = {498–510},
numpages = {13},
keywords = {Mobile UI summarization, dataset., deep learning, language-based UI, screen understanding},
location = {Virtual Event, USA},
series = {UIST '21}
}
@inproceedings{SpotTheDiff,
title = "Learning to Describe Differences Between Pairs of Similar Images",
author = "Jhamtani, Harsh and
others",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1436",
doi = "10.18653/v1/D18-1436",
pages = "4024--4034",
}
@INPROCEEDINGS{STVQA,
author={Biten, Ali Furkan and Tito, Rubèn and Mafla, Andrés and Gomez, Lluis and Rusiñol, Marçal and Jawahar, C.V. and Valveny, Ernest and Karatzas, Dimosthenis},
booktitle={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
title={Scene Text Visual Question Answering},
year={2019},
volume={},
number={},
pages={4290-4300},
keywords={Visualization;Task analysis;Knowledge discovery;Text recognition;Cognition;Computer vision;Semantics},
doi={10.1109/ICCV.2019.00439}
}
@inproceedings{TabMWP,
title={Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning},
author={Lu, Pan and Qiu, Liang and Chang, Kai-Wei and Wu, Ying Nian and Zhu, Song-Chun and Rajpurohit, Tanmay and Clark, Peter and Kalyan, Ashwin},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023}
}
@inproceedings{TallyQA,
title={TallyQA: Answering Complex Counting Questions},
author={Acharya, Manoj and Kafle, Kushal and Kanan, Christopher},
booktitle={AAAI},
year={2019}
}
@inproceedings{TAT-QA,
title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance",
author = "Zhu, Fengbin and
Lei, Wenqiang and
Huang, Youcheng and
Wang, Chao and
Zhang, Shuo and
Lv, Jiancheng and
Feng, Fuli and
Chua, Tat-Seng",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.254",
doi = "10.18653/v1/2021.acl-long.254",
pages = "3277--3287"
}
@misc{textcaps,
title={TextCaps: a Dataset for Image Captioning with Reading Comprehension},
author={Oleksii Sidorov and Ronghang Hu and Marcus Rohrbach and Amanpreet Singh},
year={2020},
eprint={2003.12462},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{textvqa,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
}
@INPROCEEDINGS{TQA,
author={Kembhavi, Aniruddha and Seo, Minjoon and Schwenk, Dustin and Choi, Jonghyun and Farhadi, Ali and Hajishirzi, Hannaneh},
booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension},
year={2017},
volume={},
number={},
pages={5376-5384},
keywords={Knowledge discovery;Visualization;Cognition;Training;Natural languages;Computer vision},
doi={10.1109/CVPR.2017.571}
}
@inproceedings{VisText,
title = {{VisText: A Benchmark for Semantically Rich Chart Captioning}},
author = {Benny J. Tang AND Angie Boggust AND Arvind Satyanarayan},
booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)},
year = {2023},
url = {http://vis.csail.mit.edu/pubs/vistext}
}
@InProceedings{Visual7w,
title = {{Visual7W: Grounded Question Answering in Images}},
author = {Yuke Zhu and Oliver Groth and Michael Bernstein and Li Fei-Fei},
booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition}},
year = 2016,
}
@inproceedings{VisualMRC,
author = {Ryota Tanaka and
Kyosuke Nishida and
Sen Yoshida},
title = {VisualMRC: Machine Reading Comprehension on Document Images},
booktitle = {AAAI},
year = {2021}
}
@article{VQA-RAD,
author = {Lau, Jason and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina},
year = {2018},
month = {11},
pages = {180251},
title = {A dataset of clinically generated visual questions and answers about radiology images},
volume = {5},
journal = {Scientific Data},
doi = {10.1038/sdata.2018.251}
}
@misc{VQAv2,
title={Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering},
author={Yash Goyal and Tejas Khot and Douglas Summers-Stay and Dhruv Batra and Devi Parikh},
year={2017},
eprint={1612.00837},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{VSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Emerson and Nigel Collier},
year={2023},
eprint={2205.00363},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{WebSight,
title={Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset},
author={Hugo Laurençon and Léo Tronchon and Victor Sanh},
year={2024},
eprint={2403.09029},
archivePrefix={arXiv},
primaryClass={cs.HC}
}
</details>
## Licensing Information
Each of the publicly available sub-datasets present in the Cauldron are governed by specific licensing conditions. Therefore, when making use of them you must take into consideration each of the licenses governing each dataset.
To the extent we have any rights in the prompts, these are licensed under CC-BY-4.0.