Behind Maya: Building a Multilingual Vision Language Model
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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Nahid Alam
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Karthik Reddy Kanjula
claimed_verified
2025-05-16T07:12:15.938Z
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Karthik
user
kkr5155
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Surya Guthikonda
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Timothy Chung
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Bala Krishna S Vegesna
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Abhipsha Das
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Anthony Susevski
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Ryan Sze-Yin Chan
claimed_verified
2025-05-20T19:29:44.470Z
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Ryan Chan
user
rchan26
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S M Iftekhar Uddin
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Shayekh Bin Islam
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Roshan Santhosh
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Snegha A
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Drishti Sharma
claimed_verified
2025-05-22T07:18:20.220Z
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Drishti Sharma
user
DrishtiSharma
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Chen Liu
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Isha Chaturvedi
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Genta Indra Winata
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Ashvanth. S
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Snehanshu Mukherjee
68262a5ea5284bb5e477447d
Alham Fikri Aji
Behind Maya: Building a Multilingual Vision Language Model
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.
Maya, a multilingual Vision-Language Model, enhances performance on low-resource languages and varied cultural contexts using a multilingual image-text pretraining dataset.
Vision-Language Models multilingual image-text pretraining dataset LLaVA pretraining dataset multilingual image-text model