The complementarity of multimodal signal is essential for video anomaly detection.
However, existing methods either lack exploration to multimodal data or ignore the implicit …

Unsupervised approaches for video anomaly detection may not perform as good as
supervised approaches. However, learning unknown types of anomalies using an …

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 …

Video anomaly detection, which is weakly supervised by video-level annotations, is a
frequent yet challenging task in computer vision owing to its unexpectedness, equivocality …

Recent progress in video anomaly detection (VAD) has shown that feature discrimination is
the key to effectively distinguishing anomalies from normal events. We observe that many …

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 …

The existing anomaly detection methods can be divided into two popular models based on
reconstruction or future frame prediction. Due to the strong learning capacity, reconstruction …

This paper introduces an innovative end-to-end trainable framework named Dense Attention-
aware Spatio-Temporal Network (DAST-Net) for video anomaly detection. The framework …

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 …

Video anomaly detection aims to automatically identify unusual objects or behaviours by
learning from normal videos. Previous methods tend to use simplistic reconstruction or …