论文标题
爪：聚类辅助弱监督的学习，正常抑制异常事件检测
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
论文作者
论文摘要
通过视频级标签来检测现实世界中的异常事件，这是一项艰巨的任务，因为罕见的标签异常和噪声发生。在这项工作中，我们提出了一种弱监督的异常检测方法，该方法具有多种贡献，包括1）一种基于批次的培训程序，以减少批量间相关性，2）一种正常抑制机制，2）最小化视频正常区域的异常分数，通过将视频的正常区域评估通过，以一种在一个培训批次中获得较大的差异，并促进距离造成距离的整体信息，以及3），以及3），以及3），以及3），以及3）造成距离的差异，以及3），以及3）的差异，并贡献了距离的距离。我们的模型生成不同的正常和异常簇。该方法分别在UCF犯罪和Shanghaitech数据集上获得了83.03％和89.67％的帧级AUC性能，证明了其优于现有的最新算法。
Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
