Video anomaly detection (VAD) involves identifying events or behaviours in video
sequences that deviate from expected patterns. Most VAD models to date focus on seeking …

Video anomaly detection (VAD) without human monitoring is a complex computer vision task
that can have a positive impact on society if implemented successfully. While recent …

Video anomaly detection (VAD) is a challenging computer vision task with many practical
applications. As anomalies are inherently ambiguous, it is essential for users to understand …

Anomalies are rare and anomaly detection is often therefore framed as One-Class
Classification (OCC), ie trained solely on normalcy. Leading OCC techniques constrain the …

Anomaly detection in videos is a significant yet challenging problem. Previous approaches
based on deep neural networks employ either reconstruction-based or prediction-based …

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 …

Multi-task learning based video anomaly detection methods combine multiple proxy tasks in
different branches to detect video anomalies in different situations. Most existing methods …

The existing methods for video anomaly detection mostly utilize videos containing
identifiable facial and appearance-based features. The use of videos with identifiable faces …

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 …

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …