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 …

We investigate and analyze methods to violence detection in this study to completely
disassemble the present condition and anticipate the emerging trends of violence discovery …

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep
neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or …

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 …

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

Weakly supervised detection of anomalies in surveillance videos is a challenging task.
Going beyond existing works that have deficient capabilities to localize anomalies in long …

Abstract Weakly supervised Video Anomaly Detection (VAD) using Multi-Instance Learning
(MIL) is usually based on the fact that the anomaly score of an abnormal snippet is higher …

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

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 (VAD) aims at localizing unexpected actions or activities in a video
sequence. Existing mainstream VAD techniques are based on either the one-class …