4 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…
Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement
Shuangli Du, Siming Yan, Zhenghao Shi +2
Low-light images suffer from complex degradation, and existing enhancement methods often encode all degradation factors within a single latent space. This leads to highly entangled…
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…