Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this
work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware …

Anomaly detection in the video is an important research area and a challenging task in real
applications. Due to the unavailability of large-scale annotated anomaly events, most …

Recent progress in video anomaly detection (VAD) has shown that feature discrimination is
the key to effectively distinguishing anomalies from normal events. We observe that many …

Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from
normal events based on discriminative representations. Most existing works are limited in …

As a vital topic in media content interpretation, video anomaly detection (VAD) has made
fruitful progress via deep neural network (DNN). However, existing methods usually follow a …

Video anomaly detection is to automatically identify predefined anomalous contents (eg
abnormal objects, behaviors and scenes) in videos. The performance of video anomaly …

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep
neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or …

Current video anomaly detection (VAD) approaches with weak supervisions are inherently
limited to a closed-set setting and may struggle in open-world applications where there can …

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent
surveillance system. However, an essential type of anomaly in VAD named scene …

Video anomaly detection is a challenging task in the Computer vision community. Most
single task-based methods do not consider the independence of unique spatial and …