This paper introduces the Learned User Significance Tracker (LUST), a framework designed to analyze video content and quantify the thematic relevance of its segments in relation to a user-provided textual description of significance. LUST leverages a multi-modal analytical pipeline, integrating visual cues from video frames with textual information extracted via Automatic Speech Recognition (ASR) from the audio track. The core innovation lies in a hierarchical, two-stage relevance scoring mechanism employing Large Language Models (LLMs). An initial "direct relevance" score, $S_{d,i}$, assesses individual segments based on immediate visual and auditory content against the theme. This is followed by a "contextual relevance" score, $S_{c,i}$, that refines the assessment by incorporating the temporal progression of preceding thematic scores, allowing the model to understand evolving narratives. The LUST framework aims to provide a nuanced, temporally-aware measure of user-defined significance, outputting an annotated video with visualized relevance scores and comprehensive analytical logs.

翻译：本文介绍了学习型用户显著性追踪器（LUST），该框架旨在分析视频内容，并量化其片段与用户提供的文本化显著性描述之间的主题相关性。LUST采用多模态分析流程，将视频帧的视觉线索与通过音频轨道的自动语音识别（ASR）提取的文本信息相结合。其核心创新在于采用大型语言模型（LLMs）的分层两阶段相关性评分机制。初始的“直接相关性”分数$S_{d,i}$根据片段即时视觉和听觉内容与主题的匹配度进行评估。随后，“上下文相关性”分数$S_{c,i}$通过纳入先前主题评分的时间演进信息来优化评估，使模型能够理解不断发展的叙事。LUST框架旨在提供一种细致入微、具有时间感知能力的用户定义显著性度量，输出带有可视化相关性分数和全面分析日志的注释视频。