Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address these two issues, we propose a novel WSVAD method based on cross-batch clustering guidance. To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data. Meanwhile, we design a cross-batch learning strategy by introducing clustering results from previous mini-batches to reduce the impact of data imbalance. In addition, we propose to generate more accurate segment-level anomaly scores based on batch clustering guidance further improving the performance of WSVAD. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.

翻译：由于只有视频级标签可供培训使用,因此薄弱监督的视频异常探测(WSVAD)是一项具有挑战性的任务,因为只有视频级标签可供培训使用。在以前的研究中,所学特征的歧视性力量不够强,而微型批量培训战略所导致的数据不平衡被忽略。为了解决这两个问题,我们提议了一种基于交叉批量集群指导的新颖的WSVAD方法。为了增强特征的歧视性力量,我们提议以批量分组为基础进行损失,鼓励集群分支根据一批数据生成不同、正常和异常的集群。与此同时,我们设计了一种交叉学习战略,采用以前小型囊中小桶的组合结果来减少数据不平衡的影响。此外,我们提议根据批量组合指导产生更准确的分部级异常分数,进一步改进WSVAD的绩效。对两个公共数据集进行广泛的实验,显示了我们的方法的有效性。