构建多语言视觉语言模型Maya，提升低资源语言理解能力
Behind Maya: Building a Multilingual Vision Language Model
- 基于LLaVA数据集扩展八种语言的图文预训练数据
- 模型支持八种语言，在跨文化场景中表现更优
- 开源模型与数据，适合多语言研究者使用
近年来，大型视觉语言模型（VLM）发展迅速，在主流语言的学术基准上表现优异，但对低资源语言和多样化文化背景的适应性不足。为解决这一问题，我们提出Maya——一个开源的多语言视觉语言模型。主要贡献包括：1）基于LLaVA预训练数据集构建的八种语言图文预训练数据集；2）支持这八种语言的多语言图像-文本模型，增强了在视觉语言任务中的文化与语言理解能力。代码已公开于https://github.com/nahidalam/maya。
原文摘要 · Abstract (English)
In recent times, we have seen a rapid development of large Vision-Language Models (VLMs). They have shown impressive results on academic benchmarks, primarily in widely spoken languages but lack performance on low-resource languages and varied cultural contexts. To address these limitations, we introduce Maya, an open-source Multilingual VLM. Our contributions are: 1) a multilingual image-text pretraining dataset in eight languages, based on the LLaVA pretraining dataset; and 2) a multilingual image-text model supporting these languages, enhancing cultural and linguistic comprehension in vision-language tasks. Code available at https://github.com/nahidalam/maya.
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