论文标题
基于跨批量聚类指导的弱监督视频异常检测
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance
论文作者
论文摘要
弱监督的视频异常检测（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。在先前的研究中，学习特征的判别能力不够强，并且忽略了小批量训练策略所带来的数据不平衡。为了解决这两个问题，我们提出了一种基于跨批量聚类指导的新型WSVAD方法。为了增强特征的判别能力，我们提出了基于批量聚类的损失，以鼓励聚类分支，以基于一批数据生成不同的正常和异常簇。同时，我们通过引入以前的迷你批次的聚类结果来设计跨批量学习策略，以减少数据不平衡的影响。此外，我们建议基于批处理聚类指南生成更准确的片段级异常得分，以进一步改善WSVAD的性能。在两个公共数据集上进行的广泛实验证明了我们方法的有效性。
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.
