Multi-Scale Continuity-Aware Refinement Network for Weakly Supervised Video Anomaly Detection

In many previous work, weakly supervised video anomaly detection is formulated as a multiple instance learning (MIL) problem, which represents the video as a bag of multiple instances. However, most MIL-based frameworks only focused on identifying anomalous events from the given instances, without considering the event continuity. Motivated by the fact that abnormal events tend to be more continuous in real-world videos, a Multi-scale Continuity-aware Refinement Network (MCR) is proposed in this paper. It utilizes the property of multi-scale continuity to refine anomaly scores by introducing differential contextual information of instances. At the same time, multi-scale attention is designed to produce a video-level weights in order to select the proper scale and fuse all scores at different scales. Experimental results of MCR show noticeable improvement on two public datasets, specifically obtaining a frame-level AUC 94.92% on ShanghaiTech dataset.