语言引导的图形表示学习,用于视频总结
IEEE transactions on pattern analysis and machine intelligence
|November 17, 2025
概括
本研究介绍了一个语言引导图形表示学习网络 (LGRLN),用于有效的视频总结. 这种新的方法提高了内容的理解,并产生了定制的摘要,优于现有的方法.
科学领域:
- 多媒体处理处理.
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 视频内容在社交媒体平台上迅速扩展.
- 现有的视频总结技术与全球依赖性和多式联络定制性作斗争.
- 时间框架的近距离并不总是与语义相关性保持一致.
研究的目的:
- 为视频总结提出一种新的语言引导图形表示学习网络 (LGRLN).
- 解决捕捉全球依赖和多式联运用户定制方面的挑战.
- 为了生成带有特定文本描述的视频摘要.
主要方法:
- 视频图形生成器将转换为结构图形 (向前,向后,无定向),以保持顺序和上下文.
- 一个具有双值图形卷积的图内关系推理模块识别了语义上相关的框架.
- 一个语言引导的交叉模式嵌入模块根据文本描述生成总结,使用使用EM算法解决的伯努利分布的混合物.
主要成果:
- 与现有方法相比,拟议的LGRLN方法在多个基准中显示出优异的性能.
- LGRLN显著减少了87.8%的推断时间和91.7%的模型参数.
- 该方法有效地捕捉了全球依赖关系,并使多式联运用户定制用于视频总结.
结论:
- 通过有效地整合图形表示和语言指导,LGRLN在视频总结方面取得了重大进展.
- 该模型提供了高效和可定制的视频总结解决方案.
- 这项研究为处理视频数据中的复杂关系提供了一个新的框架,用于总结任务.
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