为了捍卫基于剪辑的视频关系检测
概括
本研究介绍了用于视频视觉关系检测 (VidVRD) 的等级上下文模型 (HCM). HCM通过增强空间和时间上下文来改进基于剪辑的方法,实现最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 视频视觉关系检测 (VidVRD) 识别视频中的视觉关系三重.
- 目前的方法使用自下而上 (基于剪辑) 或自上而下 (基于视频) 的模式.
- 有效的空间和时间上下文建模对于VidVRD性能至关重要.
研究的目的:
- 重新审视和改进VidVRD的基于剪辑的范式.
- 为增强的上下文建模提出一个新的层次上下文模型 (HCM).
- 以先进的背景来证明基于剪贴的方法的优越性.
主要方法:
- 开发了一个以剪辑为重点的等级上下文模型 (HCM).
- 在剪辑中增强基于对象的空间上下文和基于关系的时间上下文.
- 为了关系分类,使用了剪贴管子而不是视频管子.
主要成果:
- 拟议的HCM在两个VidVRD基准上取得了最先进的性能.
- 使用HCM的基于剪辑的方法超过了大多数基于视频的方法.
- HCM在视频管道上表现出优势,避免了长期的跟踪问题.
结论:
- 在基于剪辑的范式中,先进的空间和时间上下文建模对VidVRD非常有效.
- 基于剪辑的HCM方法提供了灵活性,克服了视频管道的局限性.
- 这项工作重新建立了VidVRD中基于剪贴的方法的潜力.
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