Deep neural networks have achieved remarkable success for video-based action
recognition. However, most of existing approaches cannot be deployed in practice due to …

Existing video anomaly detection methods and datasets suffer from restricted anomaly
categories containing single-source (CCTV) videos recorded in controlled environment …

Video anomaly event detection (VAED) is one of the key technologies in computer vision for
smart surveillance systems. With the advent of deep learning, contemporary advances in …

Anomaly detection in surveillance videos, as a special case of video-based action
recognition, has been of increasing interest in multimedia community and public security …

Current weakly supervised video anomaly detection algorithms mostly use multiple instance
learning (MIL) or their varieties. Almost all recent approaches focus on how to select the …

Although great progress has been sparked in video anomaly detection (VAD) by deep
neural networks (DNNs), existing solutions still fall short in two aspects: 1) The extraction of …

Video anomaly detection refers to detecting anomalies automatically without manual labor,
which is of great significance to intelligent security. With the emergence of weakly …

Although various weakly supervised anomaly detection methods have been proposed in
recent years, generalization of anomaly detection is still not well-explored. Existing weakly …

Weakly-supervised video anomaly detection (WS-VAD) is a challenging task because
coarse video-level annotations are insufficient to train fine-grained (segment or frame-level) …

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 …