We propose a principled deep neural network framework with Absorbing Markov Chain
(AMC) for weakly supervised anomaly detection in surveillance videos. Our model consists …

Weakly supervised video anomaly detection is the task of detecting anomalous frames in
videos where no frame-level labels are provided at training phase. Previous methods …

Abstract Weakly supervised Video Anomaly Detection (wVAD) aims to distinguish anomalies
from normal events based on video-level supervision. Most existing works utilize Multiple …

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 aims to identify abnormal events in videos
using only video-level labels. Recently, two-stage self-training methods have achieved …

Weakly supervised video anomaly detection is to distinguish anomalies from normal scenes
and events in videos, under the setting that we only know whether there are abnormal …

Video anomaly detection refers to detecting anomalies automatically without manual labor,
which is of great significance to intelligent security. With the emergence of weakly …

Weakly-supervised Video Anomaly Detection (W-VAD) aims to detect abnormal events in
videos given only video-level labels for training. Recent methods relying on multiple …

Weakly supervised video anomaly detection is an important problem in many real-world
applications where during training there are some anomalous videos, in addition to nominal …

Video anomaly detection under weak supervision is challenging due to the absence of
frame-level annotations during the training phase. Previous work has employed graph …