Papers › VADMamba: Exploring State Space Models for Fast Video Anomaly Detection
VADMamba: Exploring State Space Models for Fast Video Anomaly Detection
Jiahao Lyu, Minghua Zhao, Jing Hu, Xuewen Huang, Yifei Chen, Shuangli Du
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 inference speed. The emergence of state space models in computer vision, exemplified by the Mamba model, demonstrates improved computational efficiency through selective scans and showcases the great potential for long-range modeling. Our study pioneers the application of Mamba to VAD, dubbed VADMamba, which is based on multi-task learning for frame prediction and optical flow reconstruction. Specifically, we propose the VQ-Mamba Unet (VQ-MaU) framework, which incorporates a Vector Quantization (VQ) layer and Mamba-based Non-negative Visual State Space (NVSS) block. Furthermore, two individual VQ-MaU networks separately predict frames and reconstruct corresponding optical flows, further boosting accuracy through a clip-level fusion evaluation strategy. Experimental results validate the efficacy of the proposed VADMamba across three benchmark datasets, demonstrating superior performance in inference speed compared to previous work. Code is available at https://github.com/jLooo/VADMamba.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Anomaly Detection | CUHK Avenue | VADMamba | AUC | 91.5% | #3 of 7 | Archive leaderboard | report |
| Video Anomaly Detection | ShanghaiTech Campus | VADMamba | AUC | 77.0% | #3 of 4 | Archive leaderboard | report |
| Video Anomaly Detection | UCSD Ped2 | VADMamba | AUC | 98.5% | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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