@inproceedings{shafique-etal-2025-culturally,
title = "A Culturally-diverse Multilingual Multimodal Video Benchmark {\&} Model",
author = "Shafique, Bhuiyan Sanjid and
Vayani, Ashmal and
Maaz, Muhammad and
Rasheed, Hanoona Abdul and
Dissanayake, Dinura and
Kurpath, Mohammed Irfan and
Hmaiti, Yahya and
Inoue, Go and
Lahoud, Jean and
Rashid, Md. Safirur and
Quasem, Shadid Intisar and
Fatima, Maheen and
Vidal, Franco and
Maslych, Mykola and
More, Ketan Pravin and
Baliah, Sanoojan and
Watawana, Hasindri and
Li, Yuhao and
Farestam, Fabian and
Schaller, Leon and
Tymtsiv, Roman and
Weber, Simon and
Cholakkal, Hisham and
Laptev, Ivan and
Satoh, Shin{'}ichi and
Felsberg, Michael and
Shah, Mubarak and
Khan, Salman and
Khan, Fahad Shahbaz",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1012/",
pages = "20009--20033",
ISBN = "979-8-89176-332-6",
abstract = "Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the English language for cultural and linguistic inclusivity is yet to be investigated in the context of video LMMs. In pursuit of more inclusive video LMMs, we introduce a multilingual Video LMM benchmark, named ViMUL-Bench, to evaluate Video LMMs across 14 languages, including both low- and high-resource languages: Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. Our ViMUL-Bench is designed to rigorously test video LMMs across 15 categories including eight culturally diverse categories, ranging from lifestyles and festivals to foods and rituals and from local landmarks to prominent cultural personalities. ViMUL-Bench comprises both open-ended (short and long-form) and multiple-choice questions spanning various video durations (short, medium, and long) with 8k samples that are manually verified by native language speakers. In addition, we also introduce a machine translated multilingual video training set comprising 1.2 million samples and develop a simple multilingual video LMM, named ViMUL, that is shown to provide a better tradeoff between high-and low-resource languages for video understanding. We hope our ViMUL-Bench and multilingual video LMM along with a large-scale multilingual video training set will help ease future research in developing cultural and linguistic inclusive multilingual video LMMs. Our proposed benchmark, video LMM and training data will be publicly released."
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<abstract>Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the English language for cultural and linguistic inclusivity is yet to be investigated in the context of video LMMs. In pursuit of more inclusive video LMMs, we introduce a multilingual Video LMM benchmark, named ViMUL-Bench, to evaluate Video LMMs across 14 languages, including both low- and high-resource languages: Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. Our ViMUL-Bench is designed to rigorously test video LMMs across 15 categories including eight culturally diverse categories, ranging from lifestyles and festivals to foods and rituals and from local landmarks to prominent cultural personalities. ViMUL-Bench comprises both open-ended (short and long-form) and multiple-choice questions spanning various video durations (short, medium, and long) with 8k samples that are manually verified by native language speakers. In addition, we also introduce a machine translated multilingual video training set comprising 1.2 million samples and develop a simple multilingual video LMM, named ViMUL, that is shown to provide a better tradeoff between high-and low-resource languages for video understanding. We hope our ViMUL-Bench and multilingual video LMM along with a large-scale multilingual video training set will help ease future research in developing cultural and linguistic inclusive multilingual video LMMs. Our proposed benchmark, video LMM and training data will be publicly released.</abstract>
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%0 Conference Proceedings
%T A Culturally-diverse Multilingual Multimodal Video Benchmark & Model
%A Shafique, Bhuiyan Sanjid
%A Vayani, Ashmal
%A Maaz, Muhammad
%A Rasheed, Hanoona Abdul
%A Dissanayake, Dinura
%A Kurpath, Mohammed Irfan
%A Hmaiti, Yahya
%A Inoue, Go
%A Lahoud, Jean
%A Rashid, Md. Safirur
%A Quasem, Shadid Intisar
%A Fatima, Maheen
%A Vidal, Franco
%A Maslych, Mykola
%A More, Ketan Pravin
%A Baliah, Sanoojan
%A Watawana, Hasindri
%A Li, Yuhao
%A Farestam, Fabian
%A Schaller, Leon
%A Tymtsiv, Roman
%A Weber, Simon
%A Cholakkal, Hisham
%A Laptev, Ivan
%A Satoh, Shin’ichi
%A Felsberg, Michael
%A Shah, Mubarak
%A Khan, Salman
%A Khan, Fahad Shahbaz
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F shafique-etal-2025-culturally
%X Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the English language for cultural and linguistic inclusivity is yet to be investigated in the context of video LMMs. In pursuit of more inclusive video LMMs, we introduce a multilingual Video LMM benchmark, named ViMUL-Bench, to evaluate Video LMMs across 14 languages, including both low- and high-resource languages: Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. Our ViMUL-Bench is designed to rigorously test video LMMs across 15 categories including eight culturally diverse categories, ranging from lifestyles and festivals to foods and rituals and from local landmarks to prominent cultural personalities. ViMUL-Bench comprises both open-ended (short and long-form) and multiple-choice questions spanning various video durations (short, medium, and long) with 8k samples that are manually verified by native language speakers. In addition, we also introduce a machine translated multilingual video training set comprising 1.2 million samples and develop a simple multilingual video LMM, named ViMUL, that is shown to provide a better tradeoff between high-and low-resource languages for video understanding. We hope our ViMUL-Bench and multilingual video LMM along with a large-scale multilingual video training set will help ease future research in developing cultural and linguistic inclusive multilingual video LMMs. Our proposed benchmark, video LMM and training data will be publicly released.
%U https://aclanthology.org/2025.emnlp-main.1012/
%P 20009-20033
Markdown (Informal)
[A Culturally-diverse Multilingual Multimodal Video Benchmark & Model](https://aclanthology.org/2025.emnlp-main.1012/) (Shafique et al., EMNLP 2025)
ACL
- Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath, Yahya Hmaiti, Go Inoue, Jean Lahoud, Md. Safirur Rashid, Shadid Intisar Quasem, Maheen Fatima, Franco Vidal, Mykola Maslych, Ketan Pravin More, Sanoojan Baliah, Hasindri Watawana, Yuhao Li, Fabian Farestam, Leon Schaller, Roman Tymtsiv, Simon Weber, Hisham Cholakkal, Ivan Laptev, Shin’ichi Satoh, Michael Felsberg, Mubarak Shah, Salman Khan, and Fahad Shahbaz Khan. 2025. A Culturally-diverse Multilingual Multimodal Video Benchmark & Model. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 20009–20033, Suzhou, China. Association for Computational Linguistics.