Video anomaly detection aims to identify abnormal events that occurred in videos. Since anomalous events are relatively rare, it is not feasible to collect a balanced dataset and train a binary classifier to solve the task. Thus, most previous approaches learn only from normal videos using unsupervised or semi-supervised methods. Obviously, they are limited in capturing and utilizing discriminative abnormal characteristics, which leads to compromised anomaly detection performance. In this paper, to address this issue, we propose a new learning paradigm by making full use of both normal and abnormal videos for video anomaly detection. In particular, we formulate a new learning task: cross-domain few-shot anomaly detection, which can transfer knowledge learned from numerous videos in the source domain to help solve few-shot abnormality detection in the target domain. Concretely, we leverage self-supervised training on the target normal videos to reduce the domain gap and devise a meta context perception module to explore the video context of the event in the few-shot setting. Our experiments show that our method significantly outperforms baseline methods on DoTA and UCF-Crime datasets, and the new task contributes to a more practical training paradigm for anomaly detection.

翻译：由于异常事件相对少见,因此收集平衡的数据集和培训二进制分类器来完成这项任务是不可行的。因此,大多数以往的做法仅使用未经监督或半监督的方法从普通视频中学习。显然,它们捕捉和利用歧视性异常特征有限,导致异常现象检测性工作受损。在本文中,为了解决这一问题,我们建议了一种新的学习模式,充分利用正常和异常视频来探测视频异常。特别是,我们制定了一个新的学习任务:跨部小片异常探测,这可以转让从源域许多视频中学到的知识,帮助解决目标域内少数截图异常检测问题。具体地说,我们利用目标正常视频的自我监督培训来缩小域间差距,并设计一个元背景认知模块,以探索在微小镜头下发现事件的视频背景。我们的实验表明,我们的方法大大超出了DoTA和UCF-D数据集的基准方法,而新的任务有助于为异常现象检测提供更实用的培训模式。