Video anomaly detection under video-level labels is currently a challenging task. Previous works have made progresses on discriminating whether video sequencecontains anomalies. However, most of them fail to accurately localize the anomalous events within videos in temporal domain. In this paper, we propose Weakly Supervised Anomaly Localization (WSAL) method focusing temporally localizing segm...