@inproceedings{chen-etal-2025-videovista,
title = "{V}ideo{V}ista-{C}ultural{L}ingo: 360{\textdegree} Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension",
author = "Chen, Xinyu and
Li, Yunxin and
Shi, Haoyuan and
Hu, Baotian and
Luo, Wenhan and
Wang, Yaowei and
Zhang, Min",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1315/",
doi = "10.18653/v1/2025.acl-long.1315",
pages = "27102--27128",
ISBN = "979-8-89176-251-0",
abstract = "Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are limited to a single language, typically English, and predominantly feature videos rooted in Western cultural contexts. In this paper, we present **VideoVista-CulturalLingo**, the first video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. Our work differs from existing benchmarks in the following ways: 1) **Cultural diversity**, incorporating cultures from China, North America, and Europe; 2) **Multi-linguistics**, with questions presented in Chinese and English{---}two of the most widely spoken languages; and 3) **Broad domain**, featuring videos sourced from hundreds of human-created domains. VideoVista-CulturalLingo contains 1,389 videos and 3,134 QA pairs, and we have evaluated 24 recent open-source or proprietary video large models. From the experiment results, we observe that: 1) Existing models perform worse on Chinese-centric questions than Western-centric ones, particularly those related to Chinese history; 2) Current open-source models still exhibit limitations in temporal understanding, especially in the Event Localization task, achieving a maximum score of only 45.2{\%}; 3) Mainstream models demonstrate strong performance in general scientific questions, while open-source models demonstrate weak performance in mathematics."
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<abstract>Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are limited to a single language, typically English, and predominantly feature videos rooted in Western cultural contexts. In this paper, we present **VideoVista-CulturalLingo**, the first video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. Our work differs from existing benchmarks in the following ways: 1) **Cultural diversity**, incorporating cultures from China, North America, and Europe; 2) **Multi-linguistics**, with questions presented in Chinese and English—two of the most widely spoken languages; and 3) **Broad domain**, featuring videos sourced from hundreds of human-created domains. VideoVista-CulturalLingo contains 1,389 videos and 3,134 QA pairs, and we have evaluated 24 recent open-source or proprietary video large models. From the experiment results, we observe that: 1) Existing models perform worse on Chinese-centric questions than Western-centric ones, particularly those related to Chinese history; 2) Current open-source models still exhibit limitations in temporal understanding, especially in the Event Localization task, achieving a maximum score of only 45.2%; 3) Mainstream models demonstrate strong performance in general scientific questions, while open-source models demonstrate weak performance in mathematics.</abstract>
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%0 Conference Proceedings
%T VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension
%A Chen, Xinyu
%A Li, Yunxin
%A Shi, Haoyuan
%A Hu, Baotian
%A Luo, Wenhan
%A Wang, Yaowei
%A Zhang, Min
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F chen-etal-2025-videovista
%X Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are limited to a single language, typically English, and predominantly feature videos rooted in Western cultural contexts. In this paper, we present **VideoVista-CulturalLingo**, the first video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. Our work differs from existing benchmarks in the following ways: 1) **Cultural diversity**, incorporating cultures from China, North America, and Europe; 2) **Multi-linguistics**, with questions presented in Chinese and English—two of the most widely spoken languages; and 3) **Broad domain**, featuring videos sourced from hundreds of human-created domains. VideoVista-CulturalLingo contains 1,389 videos and 3,134 QA pairs, and we have evaluated 24 recent open-source or proprietary video large models. From the experiment results, we observe that: 1) Existing models perform worse on Chinese-centric questions than Western-centric ones, particularly those related to Chinese history; 2) Current open-source models still exhibit limitations in temporal understanding, especially in the Event Localization task, achieving a maximum score of only 45.2%; 3) Mainstream models demonstrate strong performance in general scientific questions, while open-source models demonstrate weak performance in mathematics.
%R 10.18653/v1/2025.acl-long.1315
%U https://aclanthology.org/2025.acl-long.1315/
%U https://doi.org/10.18653/v1/2025.acl-long.1315
%P 27102-27128
Markdown (Informal)
[VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension](https://aclanthology.org/2025.acl-long.1315/) (Chen et al., ACL 2025)
ACL
- Xinyu Chen, Yunxin Li, Haoyuan Shi, Baotian Hu, Wenhan Luo, Yaowei Wang, and Min Zhang. 2025. VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27102–27128, Vienna, Austria. Association for Computational Linguistics.