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Behind Maya: Building a Multilingual Vision Language Model

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arxiv 2505.08910 v2 pith:FRVIVM4O submitted 2025-05-13 cs.CV cs.CL

Nahid Alam , Karthik Reddy Kanjula , Surya Guthikonda , Timothy Chung , Bala Krishna S Vegesna , Abhipsha Das , Anthony Susevski , Ryan Sze-Yin Chan
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S M Iftekhar Uddin Shayekh Bin Islam Roshan Santhosh Snegha A Drishti Sharma Chen Liu Isha Chaturvedi Genta Indra Winata Ashvanth.S Snehanshu Mukherjee Alham Fikri Aji
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classification cs.CVcs.CL
keywords languagesmultilingualmayaculturaldatasetimage-textmodelpretraining
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. NeoBabel: A Multilingual Open Tower for Visual Generation

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A 2B multilingual text-to-image model trained on 124M translated pairs matches or beats larger English-only baselines on English while scoring higher on the authors' multilingual benchmark extensions.

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