# MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation

arXiv: https://arxiv.org/abs/2412.07147 · HTML: https://mlsanity.com/paper/2412.07147/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://mlsanity.com/paper/2403.05525/) (2024)
- [CogVLM2: Visual Language Models for Image and Video Understanding](https://mlsanity.com/paper/2408.16500/) (2024)
- [Towards Fully Automated Manga Translation](https://mlsanity.com/paper/2012.14271/) (2020)
- [Towards End-to-End In-Image Neural Machine Translation](https://mlsanity.com/paper/2010.10648/) (2020)
- [Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task](https://mlsanity.com/paper/2210.03887/) (2022)
- [DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms](https://mlsanity.com/paper/2406.07232/) (2024)
- [Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation](https://mlsanity.com/paper/2407.02894/) (2024)
- [E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation](https://mlsanity.com/paper/2305.05166/) (2023)

## Built on by (most cited first, 12 of 12)

- [PRIM: Towards Practical In-Image Multilingual Machine Translation](https://mlsanity.com/paper/2509.05146/) (2025)
- [MT3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2505.19714/) (2025)
- [Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation](https://mlsanity.com/paper/2506.11820/) (2025)
- [Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation](https://mlsanity.com/paper/2602.21956/) (2026)
- [IMTBench: A Multi-Scenario Cross-Modal Collaborative Evaluation Benchmark for In-Image Machine Translation](https://mlsanity.com/paper/2603.10495/) (2026)
- [MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation](https://mlsanity.com/paper/2604.16943/) (2026)
- [SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment](https://mlsanity.com/paper/2606.25821/) (2026)
- [MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference](https://mlsanity.com/paper/2605.05225/) (2026)
- [HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track](https://mlsanity.com/paper/2507.19616/) (2025)
- [VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation](https://mlsanity.com/paper/2605.24675/) (2026)
- [Comparative Evaluation of Machine Translation Systems on Images with Text](https://mlsanity.com/paper/2605.29476/) (2026)
- [VISTA-Bench: Benchmarking Multilingual Image Translation with Image-Specific Rubrics](https://mlsanity.com/paper/2609.37287/) (2026)

## Abstract (arXiv)

Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets often suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. To address this issue, we introduce MIT-10M, a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data, which has undergone extensive data cleaning and multilingual translation validation. It contains 840K images in three sizes, 28 categories, tasks with three levels of difficulty and 14 languages image-text pairs, which is a considerable improvement on existing datasets. We conduct extensive experiments to evaluate and train models on MIT-10M. The experimental results clearly indicate that our dataset has higher adaptability when it comes to evaluating the performance of the models in tackling challenging and complex image translation tasks in the real world. Moreover, the performance of the model fine-tuned with MIT-10M has tripled compared to the baseline model, further confirming its superiority.