ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912726180167680 |
|---|---|
| author | Nie, Yuxiang Wang, Han Ye, Yongjie Yu, Haiyang Jia, Weitao Zeng, Tao Feng, Hao Fei, Xiang Li, Yang Lv, Xiaohui Tang, Guozhi Tang, Jingqun Lu, Jinghui Dai, Zehui Wang, Jiacong Yang, Dingkang Wang, An-Lan Huang, Can |
| author_facet | Nie, Yuxiang Wang, Han Ye, Yongjie Yu, Haiyang Jia, Weitao Zeng, Tao Feng, Hao Fei, Xiang Li, Yang Lv, Xiaohui Tang, Guozhi Tang, Jingqun Lu, Jinghui Dai, Zehui Wang, Jiacong Yang, Dingkang Wang, An-Lan Huang, Can |
| contents | This paper introduces ChineseVideoBench, a pioneering benchmark specifically designed for evaluating Multimodal Large Language Models (MLLMs) in Chinese Video Question Answering. The growing demand for sophisticated video analysis capabilities highlights the critical need for comprehensive, culturally-aware evaluation frameworks. ChineseVideoBench addresses this gap by providing a robust dataset and tailored evaluation metrics, enabling rigorous assessment of state-of-the-art MLLMs on complex Chinese video content. Specifically, ChineseVideoBench comprises 8 main classes and 12 sub-classes, encompassing tasks that demand both deep video understanding and nuanced Chinese linguistic and cultural awareness. Our empirical evaluations reveal that ChineseVideoBench presents a significant challenge to current MLLMs. Among the models assessed, Gemini 2.5 Pro achieves the highest performance with an overall score of 77.9%, while InternVL-38B emerges as the most competitive open-source model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering Nie, Yuxiang Wang, Han Ye, Yongjie Yu, Haiyang Jia, Weitao Zeng, Tao Feng, Hao Fei, Xiang Li, Yang Lv, Xiaohui Tang, Guozhi Tang, Jingqun Lu, Jinghui Dai, Zehui Wang, Jiacong Yang, Dingkang Wang, An-Lan Huang, Can Computer Vision and Pattern Recognition This paper introduces ChineseVideoBench, a pioneering benchmark specifically designed for evaluating Multimodal Large Language Models (MLLMs) in Chinese Video Question Answering. The growing demand for sophisticated video analysis capabilities highlights the critical need for comprehensive, culturally-aware evaluation frameworks. ChineseVideoBench addresses this gap by providing a robust dataset and tailored evaluation metrics, enabling rigorous assessment of state-of-the-art MLLMs on complex Chinese video content. Specifically, ChineseVideoBench comprises 8 main classes and 12 sub-classes, encompassing tasks that demand both deep video understanding and nuanced Chinese linguistic and cultural awareness. Our empirical evaluations reveal that ChineseVideoBench presents a significant challenge to current MLLMs. Among the models assessed, Gemini 2.5 Pro achieves the highest performance with an overall score of 77.9%, while InternVL-38B emerges as the most competitive open-source model. |
| title | ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18399 |