A Culturally-diverse Multilingual Multimodal Video Benchmark & Model

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 H. Khan; Fahad Shahbaz Khan

doi:10.18653/v1/2025.emnlp-main.1012

EMNLP2025顶会

A Culturally-diverse Multilingual Multimodal Video Benchmark & Model

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

DOI 出版方

2025年份

1被引次数

1顶会引用

摘要

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 En-* Equal contribution glish 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,