5 papers
VADMamba++: Efficient Video Anomaly Detection via Hybrid Modeling in Grayscale Space
Jihao Lyu, Minghua Zhao, Jing Hu +3
VADMamba pioneered the introduction of Mamba to Video Anomaly Detection (VAD), achieving high accuracy and fast inference through hybrid proxy tasks. Nevertheless, its heavy relian…
Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection
Jiahao Lyu, Minghua Zhao, Xuewen Huang +5
As a crucial element of public security, video anomaly detection (VAD) aims to measure deviations from normal patterns for various events in real-time surveillance systems. However…
VADMamba: Exploring State Space Models for Fast Video Anomaly Detection
Jiahao Lyu, Minghua Zhao, Jing Hu +3
Video anomaly detection (VAD) methods are mostly CNN-based or Transformer-based, achieving impressive results, but the focus on detection accuracy often comes at the expense of inf…
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection
Jiahao Lyu, Minghua Zhao, Jing Hu +4
Video anomaly detection (VAD) often learns the distribution of normal samples and detects the anomaly through measuring significant deviations, but the undesired generalization may…
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention
Jiahao Lyu, Minghua Zhao, Jing Hu +5
With the widespread deployment of video surveillance devices and the demand for intelligent system development, video anomaly detection (VAD) has become an important part of constr…