Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …

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

Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …

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 and localization are widely used in industrial manufacturing for its
efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models …

Despite most existing anomaly detection studies assume the availability of normal training
samples only, a few labeled anomaly examples are often available in many real-world …

Surveillance video anomaly detection (SVAD) is a challenging task due to the variations in
object scale, discrimination and unexpected events, the impact of the background, and the …

Most anomaly detection (AD) models are learned using only normal samples in an
unsupervised way, which may result in ambiguous decision boundary and insufficient …

Anomaly analysis is an important component of any surveillance system. In recent years, it
has drawn the attention of the computer vision and machine learning communities. In this …

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 …