Video anomaly detection (VAD) aims at identifying unusual behaviors from videos. Most of
the existing video anomaly detection methods can achieve promising performance in the …

Anomaly detection in videos has been attracting an increasing amount of attention. Despite
the competitive performance of recent methods on benchmark datasets, they typically lack …

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 …

Video Anomaly Detection (VAD) has garnered significant attention in computer vision,
especially with the exponential growth of surveillance videos. Recently, the synthetic dataset …

Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at
least few task-relevant target domain training data are available for adaptation from the …

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 …

Anomaly detection in surveillance videos is a challenging computer vision task where only
normal videos are available during training. Recent work released the first virtual anomaly …

In recent years, video anomaly detection has been extensively investigated in both
unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite …

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 …