Hugging Face's logo

Datasets:
HuggingFaceM4
/
the_cauldron

Modalities:
Image
Text
Formats:
parquet
Size:
1M - 10M
ArXiv:
Libraries:
Datasets
Dask
Dataset card Viewer Files Community
8
the_cauldron / README.md
HugoLaurencon's picture
HugoLaurencon
Upload README.md with huggingface_hub
d50c875 verified
preview code
|
raw
history blame
48.3 kB
---
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: 871997710.0
num_examples: 16539
download_size: 893265070
dataset_size: 871997710.0
- 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: 11522617868.0
num_examples: 70000
download_size: 13267429872
dataset_size: 11522617868.0
- 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: 2213960474.0
num_examples: 46287
download_size: 2393991009
dataset_size: 2213960474.0
- 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: 481233278.0
num_examples: 47974
download_size: 613100257
dataset_size: 481233278.0
- config_name: diagram_image_to_text
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: 18877197.0
num_examples: 300
download_size: 18706661
dataset_size: 18877197.0
- 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: 6885686042.0
num_examples: 10189
download_size: 6887803845
dataset_size: 6885686042.0
- 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: 3689940101.0
num_examples: 200000
download_size: 4295254110
dataset_size: 3689940101.0
- 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: 1901887152.0
num_examples: 100000
download_size: 2220036667
dataset_size: 1901887152.0
- 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: 135268568.0
num_examples: 5276
download_size: 123698250
dataset_size: 135268568.0
- 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: 951640204.0
num_examples: 9303
download_size: 323746516
dataset_size: 951640204.0
- 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: 3035059823.0
num_examples: 8500
download_size: 3054208907
dataset_size: 3035059823.0
- 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: 161130580.0
num_examples: 2500
download_size: 158295807
dataset_size: 161130580.0
- 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: 1129180352.0
num_examples: 5663
download_size: 1128935602
dataset_size: 1129180352.0
- 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: 291677986.0
num_examples: 2118
download_size: 292351760
dataset_size: 291677986.0
- 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: 3238062926.0
num_examples: 37417
download_size: 3307676486
dataset_size: 3238062926.0
- 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: 12592929433.0
num_examples: 70939
download_size: 13147641100
dataset_size: 12592929433.0
- 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: 8375492591.0
num_examples: 50426
download_size: 10838882020
dataset_size: 8375492591.0
- 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: 5467134439.0
num_examples: 165746
download_size: 6078073015
dataset_size: 5467134439.0
- 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: 7837605221.0
num_examples: 157070
download_size: 5320249066
dataset_size: 7837605221.0
- 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: 1506550467.0
num_examples: 42000
download_size: 1720691636
dataset_size: 1506550467.0
- 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: 679135952.0
num_examples: 8514
download_size: 678722272
dataset_size: 679135952.0
- 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: 5950915477.0
num_examples: 74989
download_size: 6160300141
dataset_size: 5950915477.0
- 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: 4023729236.0
num_examples: 38246
download_size: 4061523247
dataset_size: 4023729236.0
- 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: 1670723783.0
num_examples: 15730
download_size: 1346254268
dataset_size: 1670723783.0
- 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: 1643123792.0
num_examples: 8566
download_size: 1526740548
dataset_size: 1643123792.0
- 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: 696265340.0
num_examples: 17247
download_size: 720462890
dataset_size: 696265340.0
- 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: 4267143189.0
num_examples: 98680
download_size: 4662245152
dataset_size: 4267143189.0
- 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: 73213942.0
num_examples: 2199
download_size: 70862028
dataset_size: 73213942.0
- 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: 5938676115.0
num_examples: 21953
download_size: 6175419911
dataset_size: 5938676115.0
- 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: 5939437331.0
num_examples: 21953
download_size: 6175442839
dataset_size: 5939437331.0
- 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: 541250281.0
num_examples: 9969
download_size: 386023352
dataset_size: 541250281.0
- 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: 4432168161.0
num_examples: 14366
download_size: 4443083495
dataset_size: 4432168161.0
- 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: 16561537.0
num_examples: 313
download_size: 16226241
dataset_size: 16561537.0
- 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: 10630091683.0
num_examples: 82772
download_size: 13479302437
dataset_size: 10630091683.0
- 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: 107489763.0
num_examples: 2157
download_size: 107576214
dataset_size: 107489763.0
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: diagram_image_to_text
data_files:
- split: train
path: diagram_image_to_text/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
![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>
## 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.