提示VAD:通过视觉语言模型的异常提示.
IEEE transactions on neural networks and learning systems
|October 30, 2025
概括
这项研究介绍了PromptVAD,这是一种用于弱监督视频异常检测 (WSVAD) 的新方法. PromptVAD有效地利用类别名称来缩小语义差距,在基准数据集上实现最先进的性能.
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:
- 弱监督视频异常检测 (WSVAD) 模型使用视频级注释训练视频.
- 异常事件类别名称为识别异常提供了有价值的人类抽象知识.
研究的目的:
- 为了提高WSVAD,利用类别名称中的隐性知识.
- 在WSVAD中减少视觉数据和异常类别之间的语义差距.
主要方法:
- 引入了一个可学习的异常提示符,包含域名,类别和定义提示符,基于视觉语言预训练.
- 提出了PromptVAD,一种精细的WSVAD方法,利用可学习的异常提示.
- 实现了一个粗粒度的两类提示模块,用于联合粗粒度和细粒度VAD学习.
主要成果:
- 在上海科技,UCF犯罪和XD暴力数据集上,PromptVAD实现了最先进的性能.
- 在UCF-Crime数据集上表现出优异的性能,AUC为88.62%.
- 有效地减少了视觉表示和异常类别之间的语义差距.
结论:
- 提出的可学习的异常提示符有效地利用了对WSVAD的类别名称知识.
- PromptVAD为粗粒度和细粒度视频异常检测提供了一个强大的框架.
- 该方法在监督较弱的视频异常检测任务中取得了重大进展.
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