Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models
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 …
systems, enabling the temporal or spatial identification of anomalous events within videos …
Learning prompt-enhanced context features for weakly-supervised video anomaly detection
Weakly supervised video anomaly detection aims to locate abnormal activities in untrimmed
videos without the need for frame-level supervision. Prior work has utilized graph …
videos without the need for frame-level supervision. Prior work has utilized graph …
Stochastic video normality network for abnormal event detection in surveillance videos
Abstract Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised …
Weakly supervised video anomaly detection and localization with spatio-temporal prompts
Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-
level anomalous event detection with only coarse video-level annotations available. Existing …
level anomalous event detection with only coarse video-level annotations available. Existing …
Self-cooperation knowledge distillation for novel class discovery
Abstract Novel Class Discovery (NCD) aims to discover unknown and novel classes in an
unlabeled set by leveraging knowledge already learned about known classes. Existing …
unlabeled set by leveraging knowledge already learned about known classes. Existing …
Dual-modeling decouple distillation for unsupervised anomaly detection
Knowledge distillation based on student-teacher network is one of the mainstream solution
paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference …
paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference …
Sampling to distill: Knowledge transfer from open-world data
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance
student models using only the pre-trained teacher network without original training data …
student models using only the pre-trained teacher network without original training data …
Video anomaly detection via self-supervised and spatio-temporal proxy tasks learning
Q Yang, C Wang, P Liu, Z Jiang, J Li - Pattern Recognition, 2025 - Elsevier
Abstract Video Anomaly Detection (VAD) aims to identify events in videos that deviate from
typical patterns. Given the scarcity of anomalous samples, previous research has primarily …
typical patterns. Given the scarcity of anomalous samples, previous research has primarily …
Memory-enhanced appearance-motion consistency framework for video anomaly detection
Modern network communication systems extensively utilize video data for various
applications, creating a pressing need for efficient Video Anomaly Detection (VAD) …
applications, creating a pressing need for efficient Video Anomaly Detection (VAD) …
Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system
The development of modern manufacturing has raised greater demands on the accuracy,
response speed, and operating cost of industrial accident warnings. Compared to …
response speed, and operating cost of industrial accident warnings. Compared to …