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 …

The recent contrastive language-image pre-training (CLIP) model has shown great success
in a wide range of image-level tasks, revealing remarkable ability for learning powerful …

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 …

Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-
grained pseudo-labels based on weak-label and then self-training a classifier is currently a …

Video anomaly detection (VAD) has been paid increasing attention due to its potential
applications, its current dominant tasks focus on online detecting anomalies, which can be …

Interpreting camera data is key for autonomously acting systems such as autonomous
vehicles. Vision systems that operate in real-world environments must be able to understand …

Video anomaly detection is an important task for public security in the multimedia field. It
aims to distinguish events that deviate from normal patterns. As important semantic …

Abstract ecent progress in video anomaly detection suggests that the features of
appearance and motion play crucial roles in distinguishing abnormal patterns from normal …

Video anomaly detection (VAD) holds immense importance across diverse domains such as
surveillance, healthcare, and environmental monitoring. While numerous surveys focus on …

Video anomaly detection research is generally evaluated on short isolated benchmark
videos only a few minutes long. However in real-world environments security cameras …