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English(EN) Pre-Decoding Acoustic Triage for Budgeted Vision-Language Captioning of Untrimmed Egocentric Video

音频优先分类可削减自我中心视频字幕的VLM调用

研究人员开发了一种新颖的音频优先方法,用于高效字幕化长视频。该方法优先考虑音频线索,以决定哪些视频片段对视觉语言模型(VLM)的分析最相关,从而降低计算成本。通过训练系统每行动触发一次,而不是每帧触发一次,与现有的基于视觉特征或均匀采样的方法相比,该技术显著提高了行动覆盖率并减少了VLM调用,即使使用冻结的音频特征也是如此。 AI

影响 这种方法可以显著降低分析大型视频数据集的计算成本,从而在进度监控和安全等领域实现更高效的AI应用。

排序理由 该集群包含一篇详细介绍视频分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

音频优先分类可削减自我中心视频字幕的VLM调用

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该集群包含一篇详细介绍视频分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Masoud Jalayer, Changyi Li, Yu Xiao ·

    面向预算受限的未剪辑第一人称视频的预解码声学分类用于视觉语言字幕生成

    arXiv:2608.22359v1 Announce Type: cross Abstract: Automatically analyzing hours-long egocentric video is increasingly essential for progress monitoring, quality control, and safety in logistics, construction, and manufacturing. Yet current pipelines that process short, fixed-size…