MMVU：衡量专家级多学科视频理解
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
AI总结：
MMVU是一个专家级多学科视频理解基准，含3,000个专家标注问题，覆盖27个主题，评估32个前沿模型，发现o1和Gemini 2.0 Flash Thinking表现最佳但仍不及人类专家。
摘要：
我们引入了MMVU，一个全面的专家级、多学科基准，用于评估基础模型在视频理解方面的能力。MMVU包含3,000个由专家标注的问题，涵盖四个核心学科下的27个主题：科学、医疗保健、人文与社会科学以及工程。与之前的基准相比，MMVU具有三个关键进步。首先，它挑战模型应用领域特定知识并进行专家级推理来分析专业领域视频，超越了当前视频基准中通常评估的基本视觉感知。其次，每个示例均由人类专家从头开始标注。我们实施了严格的数据质量控制，以确保数据集的高质量。最后，每个示例都附有专家标注的推理依据和相关领域知识，便于深入分析。我们在MMVU上对32个前沿多模态基础模型进行了广泛评估。最新的具备系统2能力的模型，o1和Gemini 2.0 Flash Thinking，在测试模型中取得了最高性能。然而，它们仍然无法与人类专业知识相匹配。通过深入的错误分析和案例研究，我们为未来在专业领域的专家级、知识密集型视频理解方面的进展提供了可行的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
