File size: 10,655 Bytes
23bc0aa 4edbba4 23bc0aa 01fa460 23bc0aa 4edbba4 01fa460 23bc0aa 9480872 23bc0aa 9480872 4edbba4 bfb0b0b 9480872 4edbba4 9480872 4edbba4 9480872 4edbba4 9480872 bfb0b0b 9480872 4edbba4 9480872 4edbba4 9480872 4edbba4 9480872 7cdca63 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 34f4ae4 9480872 d729e88 9480872 23bc0aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 |
---
language:
- multilingual
- ar
- de
- vi
- ja
- ko
- fr
- ru
- it
- th
license: cc-by-nc-4.0
size_categories:
- 10K<n<100K
task_categories:
- visual-question-answering
- image-to-text
tags:
- multilingual
- text-centric
- vqa
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: string
- name: qa_pairs
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 3078399368.832
num_examples: 6678
- name: test
num_bytes: 1052451409.396
num_examples: 2116
download_size: 4239693120
dataset_size: 4130850778.2279997
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card
The dataset is oriented toward visual question answering of multilingual text scenes in nine languages, including Korean, Japanese, Italian, Russian, Deutsch, French, Thai, Arabic, and Vietnamese. The question-answer pairs are labeled by native annotators following a series of rules. A comprehensive description of the dataset can be found in the paper [MTVQA](https://arxiv.org/pdf/2405.11985).
## - Image Distribution
<table style="width:60%;">
<tr>
<td></td>
<td><b>KO</b></td>
<td><b>JA</b></td>
<td><b>IT</b></td>
<td><b>RU</b></td>
<td><b>DE</b></td>
<td><b>FR</b></td>
<td><b>TH</b></td>
<td><b>AR</b></td>
<td><b>VI</b></td>
<td><b>Total</b> </td>
</tr>
<tr>
<td><b>Train Images</b></td>
<td>580</td>
<td>1039</td>
<td>622</td>
<td>635</td>
<td>984</td>
<td>792</td>
<td>319</td>
<td>568</td>
<td>1139</td>
<td>6678 </td>
</tr>
<tr>
<td><b>Test Images</b></td>
<td>250</td>
<td>250</td>
<td>250</td>
<td>250</td>
<td>250</td>
<td>250</td>
<td>116</td>
<td>250</td>
<td>250</td>
<td>2116 </td>
</tr>
<tr>
<td><b>Train QA</b></td>
<td>1280</td>
<td>3332</td>
<td>2168</td>
<td>1835</td>
<td>4238</td>
<td>2743</td>
<td>625</td>
<td>1597</td>
<td>4011</td>
<td>21829 </td>
</tr>
<tr>
<td><b>Test QA</b></td>
<td>558</td>
<td>828</td>
<td>884</td>
<td>756</td>
<td>1048</td>
<td>886</td>
<td>231</td>
<td>703</td>
<td>884</td>
<td>6778</td>
</tr>
</table>
## - LeaderBoard
<table style="width:75%;">
<tr>
<th>Models</th>
<td><b>AR</b></td>
<td><b><b>DE</b></td>
<td><b>FR</b></td>
<td><b>IT</b></td>
<td><b>JA</b></td>
<td><b>KO</b></td>
<td><b>RU</b></td>
<td><b>TH</b></td>
<td><b>VI</b></td>
<td><b>Average</b> </td>
</tr>
<tr>
<th align="left">GPT-4O</th>
<td>20.2 </td>
<td>34.2 </td>
<td>41.2 </td>
<td>32.7 </td>
<td>20.0 </td>
<td>33.9 </td>
<td>11.5 </td>
<td>22.5 </td>
<td>34.2 </td>
<td>27.8 </td>
</tr>
<tr>
<th align="left">Claude3 Opus</th>
<td>15.1 </td>
<td>33.4 </td>
<td>40.6 </td>
<td>34.4 </td>
<td>19.4 </td>
<td>27.2 </td>
<td>13.0 </td>
<td>19.5 </td>
<td>29.1 </td>
<td>25.7 </td>
</tr>
<tr>
<th align="left">Gemini Ultra</th>
<td>14.7 </td>
<td>32.3 </td>
<td>40.0 </td>
<td>31.8 </td>
<td>12.3 </td>
<td>17.2 </td>
<td>11.8 </td>
<td>20.3 </td>
<td>28.6 </td>
<td>23.2 </td>
</tr>
<tr>
<th align="left">GPT-4V</th>
<td>11.5 </td>
<td>31.5 </td>
<td>40.4 </td>
<td>32.3 </td>
<td>11.5 </td>
<td>16.7 </td>
<td>10.3 </td>
<td>15.0 </td>
<td>28.9 </td>
<td>22.0 </td>
</tr>
<tr>
<th align="left">QwenVL Max</th>
<td>7.7 </td>
<td>31.4 </td>
<td>37.6 </td>
<td>30.2 </td>
<td>18.6 </td>
<td>25.4 </td>
<td>10.4 </td>
<td>4.8 </td>
<td>23.5 </td>
<td>21.1 </td>
</tr>
<tr>
<th align="left">Claude3 Sonnet</th>
<td>10.5 </td>
<td>28.9 </td>
<td>35.6 </td>
<td>31.8 </td>
<td>13.9 </td>
<td>22.2 </td>
<td>11.0 </td>
<td>15.2 </td>
<td>20.8 </td>
<td>21.1 </td>
</tr>
<tr>
<th align="left">QwenVL Plus</th>
<td>4.8 </td>
<td>28.8 </td>
<td>33.7 </td>
<td>27.1 </td>
<td>12.8 </td>
<td>19.9 </td>
<td>9.4 </td>
<td>5.6 </td>
<td>18.1 </td>
<td>17.8 </td>
</tr>
<tr>
<th align="left">MiniCPM-Llama3-V-2_5</th>
<td>6.1 </td>
<td>29.6 </td>
<td>35.7 </td>
<td>26.0 </td>
<td>12.1 </td>
<td>13.1 </td>
<td>5.7 </td>
<td>12.6 </td>
<td>15.3 </td>
<td>17.3 </td>
</tr>
<tr>
<th align="left">InternVL-V1.5</th>
<td>3.4 </td>
<td>27.1 </td>
<td>31.4 </td>
<td>27.1 </td>
<td>9.9 </td>
<td>9.0 </td>
<td>4.9 </td>
<td>8.7 </td>
<td>12.4 </td>
<td>14.9 </td>
</tr>
<tr>
<th align="left">GLM4V</th>
<td>0.3 </td>
<td>30.0 </td>
<td>34.1 </td>
<td>30.1 </td>
<td>3.4 </td>
<td>5.7 </td>
<td>3.0 </td>
<td>3.5 </td>
<td>12.3 </td>
<td>13.6 </td>
</tr>
<tr>
<th align="left">TextSquare</th>
<td>3.7 </td>
<td>27.0 </td>
<td>30.8 </td>
<td>26.7 </td>
<td>3.2 </td>
<td>7.2 </td>
<td>6.7 </td>
<td>5.2 </td>
<td>12.4 </td>
<td>13.6 </td>
</tr>
<tr>
<th align="left">Mini-Gemini-HD-34B</th>
<td>2.2 </td>
<td>25.0 </td>
<td>29.2 </td>
<td>25.5 </td>
<td>6.1 </td>
<td>8.6 </td>
<td>4.1 </td>
<td>4.3 </td>
<td>11.8 </td>
<td>13.0 </td>
</tr>
<tr>
<th align="left">InternLM-Xcomposer2-4KHD</th>
<td>2.0 </td>
<td>20.6 </td>
<td>23.2 </td>
<td>21.6 </td>
<td>5.6 </td>
<td>7.7 </td>
<td>4.1 </td>
<td>6.1 </td>
<td>10.1 </td>
<td>11.2 </td>
</tr>
<tr>
<th align="left">Llava-Next-34B</th>
<td>3.3 </td>
<td>24.0 </td>
<td>28.0 </td>
<td>22.3 </td>
<td>3.6 </td>
<td>6.1 </td>
<td>2.6 </td>
<td>0.4 </td>
<td>9.8 </td>
<td>11.1 </td>
</tr>
<tr>
<th align="left">TextMonkey</th>
<td>2.0 </td>
<td>18.1 </td>
<td>19.9 </td>
<td>22.1 </td>
<td>4.6 </td>
<td>7.2 </td>
<td>3.2 </td>
<td>0.9 </td>
<td>11.1 </td>
<td>9.9 </td>
</tr>
<tr>
<th align="left">MiniCPM-V-2</th>
<td>1.3 </td>
<td>12.7 </td>
<td>14.9 </td>
<td>17.0 </td>
<td>3.7 </td>
<td>5.6 </td>
<td>2.2 </td>
<td>2.2 </td>
<td>6.8 </td>
<td>7.4 </td>
</tr>
<tr>
<th align="left">mPLUG-DocOwl 1.5</th>
<td>1.0 </td>
<td>13.9 </td>
<td>14.9 </td>
<td>18.2 </td>
<td>2.9 </td>
<td>5.0 </td>
<td>2.0 </td>
<td>0.9 </td>
<td>6.4 </td>
<td>7.2 </td>
</tr>
<tr>
<th align="left">YI-VL-34B</th>
<td>1.7 </td>
<td>13.5 </td>
<td>15.7 </td>
<td>12.1 </td>
<td>4.8 </td>
<td>5.2 </td>
<td>0.8 </td>
<td>3.5 </td>
<td>4.1 </td>
<td>6.8 </td>
</tr>
<tr>
<th align="left">DeepSeek-VL</th>
<td>0.6 </td>
<td>14.2 </td>
<td>15.3 </td>
<td>15.2 </td>
<td>2.9 </td>
<td>3.8 </td>
<td>1.6 </td>
<td>0.9 </td>
<td>5.2 </td>
<td>6.6 </td>
</tr>
</table>
## - Direct usage
The data is designed to evaluate and enhance the multilingual textual vqa capabilities of multimodal models in the hope of facilitating the understanding of multilingual images, enabling AI to reach more people in the world.
### -- Huggingface dataloader
```
from datasets import load_dataset
dataset = load_dataset("ByteDance/MTVQA")
```
## - Out-of-Scope usage
Academic use only, not supported for commercial usage.
## - Ethics Assessment
Both GPT4V and manual assessment are employed to filter out unethical question and answer pairs.
## - Bias, Risks, and Limitations
Your access to and use of this dataset are at your own risk. We do not guarantee the accuracy of this dataset. The dataset is provided “as is” and we make no warranty or representation to you with respect to it and we expressly disclaim, and hereby expressly waive, all warranties, express, implied, statutory or otherwise. This includes, without limitation, warranties of quality, performance, merchantability or fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. In no event will we be liable to you on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this public license or use of the licensed material. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
## - Citation
```
@misc{tang2024mtvqa,
title={MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering},
author={Jingqun Tang and Qi Liu and Yongjie Ye and Jinghui Lu and Shu Wei and Chunhui Lin and Wanqing Li and Mohamad Fitri Faiz Bin Mahmood and Hao Feng and Zhen Zhao and Yanjie Wang and Yuliang Liu and Hao Liu and Xiang Bai and Can Huang},
year={2024},
eprint={2405.11985},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|