少即是多：基于标签的程序性和教学视频摘要
Less is More: Label-Guided Summarization of Procedural and Instructional Videos
AI总结：
PRISM提出了一种基于标签的视频摘要方法，通过集成语义和多模态分析，生成语义准确且上下文连贯的摘要，有效提升摘要质量。
摘要：
视频摘要有助于将长视频转化为清晰简洁的表示，使其更容易审查、记录和分析，特别是在高风险领域如手术培训中。先前的工作从使用基本视觉特征如颜色、运动和结构变化，发展到使用预训练的视觉-语言模型，这些模型能够更好地理解视频中的内容（语义）并捕捉时间流动，从而产生更具上下文意识的视频摘要。我们提出了一种三阶段框架，PRISM：通过集成语义和多模态分析的程序性表示，以产生语义扎根的视频摘要。PRISM结合了自适应视觉采样、标签驱动的关键帧锚定和使用大型语言模型（LLM）的上下文验证。我们的方法确保所选帧反映有意义和程序性转换，同时过滤掉通用或幻觉内容，从而在程序性和教学视频上产生上下文一致的摘要。我们在教学和活动数据集上评估了我们的方法，使用参考摘要进行教学视频。尽管只采样了原始帧的不到5%，我们的摘要保留了84%的语义内容，同时在基线之上提高了33%。我们的方法在程序性和领域特定视频任务上具有泛化能力，通过语义对齐和精度实现了强劲的性能。
英文摘要：
Video summarization helps turn long videos into clear, concise representations that are easier to review, document, and analyze, especially in high-stakes domains like surgical training. Prior work has progressed from using basic visual features like color, motion, and structural changes to using pre-trained vision-language models that can better understand what's happening in the video (semantics) and capture temporal flow, resulting in more context-aware video summarization. We propose a three-stage framework, PRISM: Procedural Representation via Integrated Semantic and Multimodal analysis, that produces semantically grounded video summaries. PRISM combines adaptive visual sampling, label-driven keyframe anchoring, and contextual validation using a large language model (LLM). Our method ensures that selected frames reflect meaningful and procedural transitions while filtering out generic or hallucinated content, resulting in contextually coherent summaries across both domain-specific and instructional videos. We evaluate our method on instructional and activity datasets, using reference summaries for instructional videos. Despite sampling fewer than 5% of the original frames, our summaries retain 84% semantic content while improving over baselines by as much as 33%. Our approach generalizes across procedural and domain-specific video tasks, achieving strong performance with both semantic alignment and precision.
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。
