MTVQA
MTVQA (Multilingual Text-Centric Visual Question Answering) is the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. It addresses visual-textual misalignment problems in multilingual text-centric VQA.
Qwen2-VL-72B-Instruct from Alibaba Cloud / Qwen Team currently leads the MTVQA leaderboard with a score of 0.309 across 1 evaluated AI models.
What MTVQA measures
MTVQA is a multimodal benchmark that evaluates large language models on multimodal, text-to-image, and vision tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.3, with the leader reaching 0.3.
Compare leaders on the best AI for multimodal, best AI for text-to-image and best AI for vision leaderboards.
Publication
- Paper
- MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
- Authors
- Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, and 13 others
- Published
- arXiv
- 2405.11985
Abstract
Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks have focused on high-resource languages like English and Chinese. Despite pioneering works to expand multilingual QA pairs in non-text-centric VQA datasets through translation engines, the translation-based protocol encounters a substantial "visual-textual misalignment" problem when applied to TEC-VQA. Specifically, it prioritizes the text in question-answer pairs while disregarding the visual text present in images. Moreover, it fails to address complexities related to nuanced meaning, contextual distortion, language bias, and question-type diversity. In this work, we tackle multilingual TEC-VQA by introducing MTVQA, the first benchmark featuring high-quality human expert annotations across 9 diverse languages, consisting of 6,778 question-answer pairs across 2,116 images. Further, by comprehensively evaluating numerous state-of-the-art Multimodal Large Language Models~(MLLMs), including Qwen2-VL, GPT-4o, GPT-4V, Claude3, and Gemini, on the MTVQA benchmark, it is evident that there is still a large room for performance improvement (Qwen2-VL scoring 30.9 versus 79.7 for human performance), underscoring the value of MTVQA. Additionally, we supply multilingual training data within the MTVQA dataset, demonstrating that straightforward fine-tuning with this data can substantially enhance multilingual TEC-VQA performance. We aspire that MTVQA will offer the research community fresh insights and stimulate further exploration in multilingual visual text comprehension. The project homepage is available at https://bytedance.github.io/MTVQA/.
Qwen2-VL-72B-Instruct leads with 30.9%.
Progress Over Time
Interactive timeline showing model performance evolution on MTVQA
MTVQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 73B | — | — |
FAQ
Common questions about MTVQA.
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