DAM : Dissimilarity Attention Module for Weakly-supervised Video Anomaly Detection

Video anomaly detection under weak supervision is complicated due to the difficulties in identifying the anomaly and normal instances during training, hence, resulting in non-optimal margin of separation. In this paper, we propose a framework consisting of Dissimilarity Attention Module (DAM) to discriminate the anomaly instances from normal ones both at feature level and score level. In order to decide instances to be normal or anomaly, DAM takes local spatio-temporal (i.e. clips within a video) dissimilarities into account rather than the global temporal context of a video. This allows the framework to detect anomalies in real-time (i.e. online) scenarios without the need of extra window buffer time. Further more, we adopt two-variants of DAM for learning the dissimilarities between successive video clips. The proposed framework along with DAM is validated on two large scale anomaly detection datasets i.e. UCF-Crime and ShanghaiTech, outperforming the online state-of-the-art approaches by 1.5% and 3.4% respectively.