ByteDance/MTVQA

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.

- Image Distribution

	KO	JA	IT	RU	DE	FR	TH	AR	VI	Total
Train Images	580	1039	622	635	984	792	319	568	1139	6678
Test Images	250	250	250	250	250	250	116	250	250	2116
Train QA	1280	3332	2168	1835	4238	2743	625	1597	4011	21829
Test QA	558	828	884	756	1048	886	231	703	884	6778

- LeaderBoard

Models	AR	DE	FR	IT	JA	KO	RU	TH	VI	Average
GPT-4O	20.2	34.2	41.2	32.7	20.0	33.9	11.5	22.5	34.2	27.8
Claude3 Opus	15.1	33.4	40.6	34.4	19.4	27.2	13.0	19.5	29.1	25.7
Gemini Ultra	14.7	32.3	40.0	31.8	12.3	17.2	11.8	20.3	28.6	23.2
GPT-4V	11.5	31.5	40.4	32.3	11.5	16.7	10.3	15.0	28.9	22.0
QwenVL Max	7.7	31.4	37.6	30.2	18.6	25.4	10.4	4.8	23.5	21.1
Claude3 Sonnet	10.5	28.9	35.6	31.8	13.9	22.2	11.0	15.2	20.8	21.1
QwenVL Plus	4.8	28.8	33.7	27.1	12.8	19.9	9.4	5.6	18.1	17.8
MiniCPM-Llama3-V-2_5	6.1	29.6	35.7	26.0	12.1	13.1	5.7	12.6	15.3	17.3
InternVL-V1.5	3.4	27.1	31.4	27.1	9.9	9.0	4.9	8.7	12.4	14.9
GLM4V	0.3	30.0	34.1	30.1	3.4	5.7	3.0	3.5	12.3	13.6
TextSquare	3.7	27.0	30.8	26.7	3.2	7.2	6.7	5.2	12.4	13.6
Mini-Gemini-HD-34B	2.2	25.0	29.2	25.5	6.1	8.6	4.1	4.3	11.8	13.0
InternLM-Xcomposer2-4KHD	2.0	20.6	23.2	21.6	5.6	7.7	4.1	6.1	10.1	11.2
Llava-Next-34B	3.3	24.0	28.0	22.3	3.6	6.1	2.6	0.4	9.8	11.1
TextMonkey	2.0	18.1	19.9	22.1	4.6	7.2	3.2	0.9	11.1	9.9
MiniCPM-V-2	1.3	12.7	14.9	17.0	3.7	5.6	2.2	2.2	6.8	7.4
mPLUG-DocOwl 1.5	1.0	13.9	14.9	18.2	2.9	5.0	2.0	0.9	6.4	7.2
YI-VL-34B	1.7	13.5	15.7	12.1	4.8	5.2	0.8	3.5	4.1	6.8
DeepSeek-VL	0.6	14.2	15.3	15.2	2.9	3.8	1.6	0.9	5.2	6.6

- 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}
}