跨领域学习用于在有限的监督下检测视频异常
Yashika Jain1, Ali Dabouei2, Min Xu2
1University of Delhi.
概括
这项研究引入了一个新的弱监督的框架,用于跨域视频异常检测 (VAD). 它通过使用外部未标记的数据来增强现有模型来提高性能,从而优于当前的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 视频异常检测 (VAD) 对于识别监控录像中不寻常事件至关重要.
- 现实世界VAD需要有效的跨领域性能,处理训练数据中代表不充分的场景.
- 目前无监督的跨域VAD方法表现出性能限制.
研究的目的:
- 为跨领域的VAD开发一个新的弱监督的框架.
- 通过利用具有成本效益的弱监管和外部未标记数据来提高跨领域的VAD性能.
- 解决现有的无监督跨领域VAD方法的局限性.
主要方法:
- 在VAD中引入了一个弱监督的跨领域学习 (CDL) 框架.
- 在培训期间纳入外部未标记的数据.
- 从外部数据估计了预测偏差,并通过预测的不确定性适应性地将其最小化.
主要成果:
- 拟议的CDL框架显著提高了跨域VAD性能.
- 在UCF-Crime数据集上实现了平均绝对改善19.6%.
- 在XD-Violence数据集上实现了12.87%的平均绝对改善,超过了最先进的方法.
结论:
- 用外部数据进行弱监督的跨领域学习为VAD提供了一个有希望的方法.
- 拟议的方法有效地增强了用于现实世界,跨领域应用的VAD模型.
- 在跨领域评估中,与现有的最先进技术相比,表现出显著的性能增长.
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