面向特定领域程序性视频摘要的多模态语言模型
Multimodal Language Models for Domain-Specific Procedural Video Summarization
AI总结：
本文针对烹饪和医疗等领域的长篇教学视频，提出微调 TimeChat 并利用 Tasty、MedVidQA 构建视频中心指令数据，以提升关键步骤提取与摘要能力。
摘要：
视频是传达想法、讲述故事和提供详细说明的强大媒介，尤其是通过长篇教程。此类教程对于按照个人节奏学习新技能非常宝贵，但其长度和密集内容可能令人难以承受。观众通常会寻找特定信息，例如精确的用量或逐步执行细节，因此高效提取和摘要关键片段至关重要。人们迫切需要一种智能的、具有时间敏感性的视频助手，能够对长视频进行摘要并检测精彩片段。多模态大语言模型的最新进展为开发这样的助手提供了有前景的解决方案。我们的研究探索如何利用多模态模型来增强特定领域内的视频摘要和逐步指令生成。这些模型需要理解跨视频帧的时间事件以及动作之间的关系。我们的方法聚焦于微调 TimeChat，以提升其在烹饪和医疗程序等特定领域的性能。通过在烹饪领域的 Tasty 和医疗程序领域的 MedVidQA 等特定领域数据集上训练模型，我们旨在增强其生成简洁、准确的教学视频摘要的能力。我们整理并重构这些数据集，以创建高质量的、以视频为中心的指令数据。研究结果表明，当在特定领域程序性数据上进行微调时，TimeChat 能够显著改进长篇视频中关键教学步骤的提取和摘要。本研究展示了专用多模态模型通过提供针对每个领域独特方面量身定制的个性化、逐步指导来协助实际任务的潜力。
英文摘要：
Videos serve as a powerful medium to convey ideas, tell stories, and provide detailed instructions, especially through long-format tutorials. Such tutorials are valuable for learning new skills at one's own pace, yet they can be overwhelming due to their length and dense content. Viewers often seek specific information, like precise measurements or step-by-step execution details, making it essential to extract and summarize key segments efficiently. An intelligent, time-sensitive video assistant capable of summarizing and detecting highlights in long videos is highly sought after. Recent advancements in Multimodal Large Language Models offer promising solutions to develop such an assistant. Our research explores the use of multimodal models to enhance video summarization and step-by-step instruction generation within specific domains. These models need to understand temporal events and relationships among actions across video frames. Our approach focuses on fine-tuning TimeChat to improve its performance in specific domains: cooking and medical procedures. By training the model on domain-specific datasets like Tasty for cooking and MedVidQA for medical procedures, we aim to enhance its ability to generate concise, accurate summaries of instructional videos. We curate and restructure these datasets to create high-quality video-centric instruction data. Our findings indicate that when finetuned on domain-specific procedural data, TimeChat can significantly improve the extraction and summarization of key instructional steps in long-format videos. This research demonstrates the potential of specialized multimodal models to assist with practical tasks by providing personalized, step-by-step guidance tailored to the unique aspects of each domain.
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。
