从理解到参与：通过视觉语言模型（VLMs）生成个性化药学视频片段
From Understanding to Engagement: Personalized pharmacy Video Clips via Vision Language Models (VLMs)
AI总结：
本文提出基于视觉语言模型和音频语言模型的个性化药学视频片段生成方法，通过剪切合并算法和个性化机制提升生成效率与质量，实现制药行业内容处理的高效自动化。
摘要：
视觉语言模型（VLMs）有潜力通过启用智能、可扩展和自动化的多模态内容处理，彻底改变制药行业的数字化转型。传统上对异构数据模态（文本、图像、视频、音频和网络链接）的手动注释容易导致不一致、质量下降和内容利用效率低下。海量的长视频和音频数据进一步加剧了这些挑战（例如，长临床试验访谈和教育研讨会）。在此，我们介绍了一个领域适应的视频到视频片段生成框架，该框架整合了音频语言模型（ALMs）和视觉语言模型（VLMs），以生成高亮片段。我们的贡献有三个方面：（i）一种可重复使用的剪切与合并算法，带有渐入渐出和时间戳标准化，确保平滑的过渡和音频/视觉对齐；（ii）基于角色定义和提示注入的个性化机制，以生成定制化输出（营销、培训、监管）；（iii）一种成本效益高的端到端管道策略，平衡ALM/VLM增强处理。在视频MME基准（900）和我们专有的16,159个药学视频数据集（涵盖14个疾病领域）上的评估显示，速度提升了3到4倍，成本降低了4倍，且片段质量具有竞争力。除了效率提升外，我们还报告了我们的方法在片段连贯性分数（0.348）和信息性分数（0.721）上优于最先进的VLM基线（例如，Gemini 2.5 Pro），突显了透明、定制提取和合规支持的视频摘要在生命科学领域的潜力。
英文摘要：
Vision Language Models (VLMs) are poised to revolutionize the digital transformation of pharmacyceutical industry by enabling intelligent, scalable, and automated multi-modality content processing. Traditional manual annotation of heterogeneous data modalities (text, images, video, audio, and web links), is prone to inconsistencies, quality degradation, and inefficiencies in content utilization. The sheer volume of long video and audio data further exacerbates these challenges, (e.g. long clinical trial interviews and educational seminars). Here, we introduce a domain adapted Video to Video Clip Generation framework that integrates Audio Language Models (ALMs) and Vision Language Models (VLMs) to produce highlight clips. Our contributions are threefold: (i) a reproducible Cut & Merge algorithm with fade in/out and timestamp normalization, ensuring smooth transitions and audio/visual alignment; (ii) a personalization mechanism based on role definition and prompt injection for tailored outputs (marketing, training, regulatory); (iii) a cost efficient e2e pipeline strategy balancing ALM/VLM enhanced processing. Evaluations on Video MME benchmark (900) and our proprietary dataset of 16,159 pharmacy videos across 14 disease areas demonstrate 3 to 4 times speedup, 4 times cost reduction, and competitive clip quality. Beyond efficiency gains, we also report our methods improved clip coherence scores (0.348) and informativeness scores (0.721) over state of the art VLM baselines (e.g., Gemini 2.5 Pro), highlighting the potential of transparent, custom extractive, and compliance supporting video summarization for life sciences.
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。
