MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Authors: name: Yilun Zhao; name: Lujing Xie; name: Haowei Zhang; name: Guo Gan; name: Yitao Long; name: Zhiyuan Hu; name: Tongyan Hu; name: Weiyuan Chen; name: Chuhan Li; name: Junyang Song; name: Zhijian Xu; name: Chengye Wang; name: Weifeng Pan; name: Ziyao Shangguan; name: Xiangru Tang; name: Zhenwen Liang; name: Yixin Liu; name: Chen Zhao; name: Arman Cohan
Published: 2025-01-21 18:56:18

Abstract: We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark
for evaluating foundation models in video understanding. MMVU includes 3,000
expert-annotated questions spanning 27 subjects across four core disciplines:
Science, Healthcare, Humanities & Social Sciences, and Engineering. Compared to
prior benchmarks, MMVU features three key advancements. First, it challenges
models to apply domain-specific knowledge and perform expert-level reasoning to
analyze specialized-domain videos, moving beyond the basic visual perception
typically assessed in current video benchmarks. Second, each example is
annotated by human experts from scratch. We implement strict data quality
controls to ensure the high quality of the dataset. Finally, each example is
enriched with expert-annotated reasoning rationals and relevant domain
knowledge, facilitating in-depth analysis. We conduct an extensive evaluation
of 32 frontier multimodal foundation models on MMVU. The latest
System-2-capable models, o1 and Gemini 2.0 Flash Thinking, achieve the highest
performance among the tested models. However, they still fall short of matching
human expertise. Through in-depth error analyses and case studies, we offer
actionable insights for future advancements in expert-level,
knowledge-intensive video understanding for specialized domains.