Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to
describe normal event patterns with small reconstruction errors. The video inputs with large …

Anomaly detection in surveillance videos, as a special case of video-based action
recognition, is an important topic in multimedia community and public security. Currently …

Video anomaly detection is a challenging task because of diverse abnormal events. To this
task, methods based on reconstruction and prediction are wildly used in recent works, which …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

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 …

Recent video anomaly detection methods focus on reconstructing or predicting frames.
Under this umbrella, the long-standing inter-class data-imbalance problem resorts to the …

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 …

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 …

Abstract Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the
binary anomaly label is only given on the video level, but the output requires snippet-level …

Recent research has shown that features obtained from pretrained Convolutional Neural
Network (CNN) models can be promptly applied to a variety of problems they were not …