title: MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
summary: 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.
paper_authors._id: 6758f7bfcbf493892e08e882
paper_authors.name: Bo Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T09:20:03.698Z
paper_authors._id: 6582c482f3006507ea10302a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6582c482f3006507ea10302a/KbgSsq0FnbMngBcWPhIXi.jpeg
paper_authors.fullname: Bo Li
paper_authors.type: user
paper_authors.user: liboaccn
paper_authors._id: 6758f7bfcbf493892e08e883
paper_authors.name: Shaolin Zhu
paper_authors._id: 6758f7bfcbf493892e08e884
paper_authors.name: Lijie Wen
paper_title: MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
paper_summary: 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.
paper_ai_summary: A new large-scale dataset, MIT-10M, significantly enhances image translation model performance and adaptability across various languages and tasks.