MIT-10M：大规模多语言图像翻译平行语料库
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
AI总结：
针对现有图像翻译数据集规模、多样性和质量不足的问题，提出大规模多语言平行语料库MIT-10M，含超1000万对真实数据，经清洗验证，支持多尺寸、多类别、多难度和多语言，微调后模型性能提升三倍。
摘要：
图像翻译（Image Translation, IT）在多个领域具有巨大潜力，能够将图像中的文本内容翻译成各种语言。然而，现有数据集通常在规模、多样性和质量方面存在局限，阻碍了图像翻译模型的开发与评估。为解决这一问题，我们提出了MIT-10M，一个大规模多语言图像翻译平行语料库，包含超过1000万对来自真实世界数据的图像-文本对，并经过了广泛的数据清洗和多语言翻译验证。该数据集包含三种尺寸的84万张图像、28个类别、三个难度级别的任务以及14种语言的图像-文本对，相比现有数据集有显著提升。我们进行了大量实验，在MIT-10M上评估和训练模型。实验结果表明，在评估模型应对现实世界中具有挑战性和复杂性的图像翻译任务时，我们的数据集具有更高的适应性。此外，使用MIT-10M微调的模型性能相比基线模型提升了三倍，进一步证实了其优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
