dataset_info:
- config_name: ai2d
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 437329975.5701048
num_examples: 2445
download_size: 439151768
dataset_size: 437329975.5701048
- config_name: aokvqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 871997710
num_examples: 16539
download_size: 893265070
dataset_size: 871997710
- config_name: chart2text
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1060645471.4167501
num_examples: 26963
download_size: 1103295177
dataset_size: 1060645471.4167501
- config_name: chartqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 784762327.9534782
num_examples: 18266
download_size: 803229473
dataset_size: 784762327.9534782
- config_name: clevr
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 11522617868
num_examples: 70000
download_size: 13267429872
dataset_size: 11522617868
- config_name: cocoqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 2213960474
num_examples: 46287
download_size: 2393991009
dataset_size: 2213960474
- config_name: datikz
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 481233278
num_examples: 47974
download_size: 613100257
dataset_size: 481233278
- config_name: docvqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 6885686042
num_examples: 10189
download_size: 6887803845
dataset_size: 6885686042
- config_name: dvqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3689940101
num_examples: 200000
download_size: 4295254110
dataset_size: 3689940101
- config_name: figureqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1901887152
num_examples: 100000
download_size: 2220036667
dataset_size: 1901887152
- config_name: finqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 135268568
num_examples: 5276
download_size: 123698250
dataset_size: 135268568
- config_name: geomverse
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 951640204
num_examples: 9303
download_size: 323746516
dataset_size: 951640204
- config_name: hateful_memes
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3035059823
num_examples: 8500
download_size: 3054208907
dataset_size: 3035059823
- config_name: hitab
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 161130580
num_examples: 2500
download_size: 158295807
dataset_size: 161130580
- config_name: iam
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1129180352
num_examples: 5663
download_size: 1128935602
dataset_size: 1129180352
- config_name: iconqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 264523321.3774117
num_examples: 27308
download_size: 326653170
dataset_size: 264523321.3774117
- config_name: infographic_vqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 291677986
num_examples: 2118
download_size: 292351760
dataset_size: 291677986
- config_name: intergps
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 25528774.867153686
num_examples: 1308
download_size: 25388245
dataset_size: 25528774.867153686
- config_name: localized_narratives
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 21380844262.41927
num_examples: 199998
download_size: 22164342699
dataset_size: 21380844262.41927
- config_name: mapqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3238062926
num_examples: 37417
download_size: 3307676486
dataset_size: 3238062926
- config_name: mimic_cgd
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 12592929433
num_examples: 70939
download_size: 13147641100
dataset_size: 12592929433
- config_name: multihiertt
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1356766489.046
num_examples: 7619
download_size: 1360814135
dataset_size: 1356766489.046
- config_name: nlvr2
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 8375492591
num_examples: 50426
download_size: 10838882020
dataset_size: 8375492591
- config_name: ocrvqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 5467134439
num_examples: 165746
download_size: 6078073015
dataset_size: 5467134439
- config_name: plotqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 7837605221
num_examples: 157070
download_size: 5320249066
dataset_size: 7837605221
- config_name: raven
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1506550467
num_examples: 42000
download_size: 1720691636
dataset_size: 1506550467
- config_name: robut_sqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 679135952
num_examples: 8514
download_size: 678722272
dataset_size: 679135952
- config_name: robut_wikisql
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 5950915477
num_examples: 74989
download_size: 6160300141
dataset_size: 5950915477
- config_name: robut_wtq
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 4023729236
num_examples: 38246
download_size: 4061523247
dataset_size: 4023729236
- config_name: scienceqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 284716288.59257776
num_examples: 4978
download_size: 283360156
dataset_size: 284716288.59257776
- config_name: screen2words
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1670723783
num_examples: 15730
download_size: 1346254268
dataset_size: 1670723783
- config_name: spot_the_diff
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1643123792
num_examples: 8566
download_size: 1526740548
dataset_size: 1643123792
- config_name: st_vqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 696265340
num_examples: 17247
download_size: 720462890
dataset_size: 696265340
- config_name: tabmwp
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 265348817.73984778
num_examples: 22723
download_size: 306648229
dataset_size: 265348817.73984778
- config_name: tallyqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 4267143189
num_examples: 98680
download_size: 4662245152
dataset_size: 4267143189
- config_name: tat_qa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 73213942
num_examples: 2199
download_size: 70862028
dataset_size: 73213942
- config_name: textcaps
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 5938676115
num_examples: 21953
download_size: 6175419911
dataset_size: 5938676115
- config_name: textvqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 5939437331
num_examples: 21953
download_size: 6175442839
dataset_size: 5939437331
- config_name: tqa
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 380346870.806369
num_examples: 1493
download_size: 378238311
dataset_size: 380346870.806369
- config_name: vistext
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 541250281
num_examples: 9969
download_size: 386023352
dataset_size: 541250281
- config_name: visual7w
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 4432168161
num_examples: 14366
download_size: 4443083495
dataset_size: 4432168161
- config_name: visualmrc
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 2941051627.2639995
num_examples: 3027
download_size: 2912911810
dataset_size: 2941051627.2639995
- config_name: vqarad
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 16561537
num_examples: 313
download_size: 16226241
dataset_size: 16561537
- config_name: vqav2
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 10630091683
num_examples: 82772
download_size: 13479302437
dataset_size: 10630091683
- config_name: vsr
features:
- name: images
sequence: image
- name: texts
list:
- name: user
dtype: string
- name: assistant
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 107489763
num_examples: 2157
download_size: 107576214
dataset_size: 107489763
configs:
- config_name: ai2d
data_files:
- split: train
path: ai2d/train-*
- config_name: aokvqa
data_files:
- split: train
path: aokvqa/train-*
- config_name: chart2text
data_files:
- split: train
path: chart2text/train-*
- config_name: chartqa
data_files:
- split: train
path: chartqa/train-*
- config_name: clevr
data_files:
- split: train
path: clevr/train-*
- config_name: cocoqa
data_files:
- split: train
path: cocoqa/train-*
- config_name: datikz
data_files:
- split: train
path: datikz/train-*
- config_name: docvqa
data_files:
- split: train
path: docvqa/train-*
- config_name: dvqa
data_files:
- split: train
path: dvqa/train-*
- config_name: figureqa
data_files:
- split: train
path: figureqa/train-*
- config_name: finqa
data_files:
- split: train
path: finqa/train-*
- config_name: geomverse
data_files:
- split: train
path: geomverse/train-*
- config_name: hateful_memes
data_files:
- split: train
path: hateful_memes/train-*
- config_name: hitab
data_files:
- split: train
path: hitab/train-*
- config_name: iam
data_files:
- split: train
path: iam/train-*
- config_name: iconqa
data_files:
- split: train
path: iconqa/train-*
- config_name: infographic_vqa
data_files:
- split: train
path: infographic_vqa/train-*
- config_name: intergps
data_files:
- split: train
path: intergps/train-*
- config_name: localized_narratives
data_files:
- split: train
path: localized_narratives/train-*
- config_name: mapqa
data_files:
- split: train
path: mapqa/train-*
- config_name: mimic_cgd
data_files:
- split: train
path: mimic_cgd/train-*
- config_name: multihiertt
data_files:
- split: train
path: multihiertt/train-*
- config_name: nlvr2
data_files:
- split: train
path: nlvr2/train-*
- config_name: ocrvqa
data_files:
- split: train
path: ocrvqa/train-*
- config_name: plotqa
data_files:
- split: train
path: plotqa/train-*
- config_name: raven
data_files:
- split: train
path: raven/train-*
- config_name: robut_sqa
data_files:
- split: train
path: robut_sqa/train-*
- config_name: robut_wikisql
data_files:
- split: train
path: robut_wikisql/train-*
- config_name: robut_wtq
data_files:
- split: train
path: robut_wtq/train-*
- config_name: scienceqa
data_files:
- split: train
path: scienceqa/train-*
- config_name: screen2words
data_files:
- split: train
path: screen2words/train-*
- config_name: spot_the_diff
data_files:
- split: train
path: spot_the_diff/train-*
- config_name: st_vqa
data_files:
- split: train
path: st_vqa/train-*
- config_name: tabmwp
data_files:
- split: train
path: tabmwp/train-*
- config_name: tallyqa
data_files:
- split: train
path: tallyqa/train-*
- config_name: tat_qa
data_files:
- split: train
path: tat_qa/train-*
- config_name: textcaps
data_files:
- split: train
path: textcaps/train-*
- config_name: textvqa
data_files:
- split: train
path: textvqa/train-*
- config_name: tqa
data_files:
- split: train
path: tqa/train-*
- config_name: vistext
data_files:
- split: train
path: vistext/train-*
- config_name: visual7w
data_files:
- split: train
path: visual7w/train-*
- config_name: visualmrc
data_files:
- split: train
path: visualmrc/train-*
- config_name: vqarad
data_files:
- split: train
path: vqarad/train-*
- config_name: vqav2
data_files:
- split: train
path: vqav2/train-*
- config_name: vsr
data_files:
- split: train
path: vsr/train-*
Dataset Card for The Cauldron
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
References to the original datasets
@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} }
Terms of Use
By using the dataset The Cauldron, you agree to comply with the original licenses of the sub-datasets it contains, as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model.
Licensing Information
License CC-BY-4.0.