Learning multiple pretext tasks is a popular approach to tackle the nonalignment problem in
unsupervised video anomaly detection. However, the conventional learning method of …

Video surveillance systems are used to enhance the public safety and private assets.
Automatic anomaly detection is vital in such surveillance systems to reduce the human labor …

We consider weakly-supervised video anomaly detection in this work. This task aims to learn
to localize video frames containing anomaly events with only binary video-level annotation …

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 …

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 …

Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

Unsupervised video anomaly detection, a task that requires no labeled normal/abnormal
training data in any form, is challenging yet of great importance to both industrial …

Existing unsupervised video anomaly detection methods often suffer from performance
degradation due to the overgeneralization of deep models. In this paper, we propose a …

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

Most video anomaly detection approaches are based on data-intensive end-to-end trained
neural networks, which extract spatiotemporal features from videos. The extracted feature …