基于与预定义文本描述相似的无监督视频异常检测
Jaehyun Kim1, Seongwook Yoon1, Taehyeon Choi1
1School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种使用文本描述和CLIP模型的新型无监督视频异常检测方法. 它实现了强大的性能,优于现有的无监督技术,而不需要广泛的数据集标签.
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:
- 传统的视频异常检测严重依赖于视频数据.
- 现实世界的应用程序从将人类领域知识纳入中受益,这些知识通常以文本形式表达.
- 没有标记的视频数据集丰富,为无监督学习提供了机会.
研究的目的:
- 探索使用文本描述用于无监督的视频异常检测.
- 开发一种利用文字在视频中识别异常情况而没有事先标记的方法.
- 通过将文本领域知识与视觉数据相结合,提高异常检测性能.
主要方法:
- 利用大型语言模型生成视频内容的文本描述.
- 采用了CLIP (对比语言-图像预训练) 视觉语言模型来计算视频和文本描述之间的等号相似性.
- 引入了使用未标记数据集和三重损失函数的文本条件相似性精细化.
主要成果:
- 拟议的方法在上海科技和UCFcrime数据集上明显优于现有的无监督方法,分别达到8%和13%的AUC得分.
- 在检测异常视频方面,其表现与监督较弱的方法相美,尽管不需要手动标签.
- 与弱监督方法相比,在异常视频上获得了17%和5%更好的AUC分数,突出了其有效性.
结论:
- 文本描述可以有效地集成到无监督的视频异常检测框架中.
- 拟议的方法为传统方法提供了计算效率高的替代方案,避免了光流或多分析.
- 这项研究验证了使用易于使用的文本描述来增强在视频数据中无监督异常检测的潜力.
关键词:
这就是CLIP CLIP.不正常的视频视频.嵌入空间空间嵌入空间预先训练的模型的微调.大型语言模型.大视觉和语言模型.相似性衡量措施相似性衡量措施文字描述 文字描述 文字描述无监督的视频异常检测检测.更多相关视频
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