# M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detection

arXiv: https://arxiv.org/abs/2511.05564 · HTML: https://mlsanity.com/paper/2511.05564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Time series forecasting with SSMs](https://mlsanity.com/ssm/time-series-forecasting-with-ssms/)

## Builds on (most cited first, 20 of 20)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection](https://mlsanity.com/paper/1903.07256/) (2019)
- [Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection](https://mlsanity.com/paper/1908.04321/) (2019)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Learning Causality-inspired Representation Consistency for Video Anomaly Detection](https://mlsanity.com/paper/2308.01537/) (2023)
- [Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model](https://mlsanity.com/paper/2412.09026/) (2024)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection](https://mlsanity.com/paper/2412.20084/) (2024)
- [VADMamba: Exploring State Space Models for Fast Video Anomaly Detection](https://mlsanity.com/paper/2503.21169/) (2025)
- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception](https://mlsanity.com/paper/2211.12636/) (2022)
- [Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images](https://mlsanity.com/paper/2408.10134/) (2024)
- [TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance](https://mlsanity.com/paper/2411.11003/) (2024)

## Built on by (most cited first, 1 of 1)

- [Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection](https://mlsanity.com/paper/2603.26745/) (2026)

## Abstract (arXiv)

Video anomaly detection (VAD) is an essential task in the image processing community with prospects in video surveillance, which faces fundamental challenges in balancing detection accuracy with computational efficiency. As video content becomes increasingly complex with diverse behavioral patterns and contextual scenarios, traditional VAD approaches struggle to provide robust assessment for modern surveillance systems. Existing methods either lack comprehensive spatial-temporal modeling or require excessive computational resources for real-time applications. In this regard, we present a Mamba-based multi-scale spatial-temporal learning (M2S2L) framework in this paper. The proposed method employs hierarchical spatial encoders operating at multiple granularities and multi-temporal encoders capturing motion dynamics across different time scales. We also introduce a feature decomposition mechanism to enable task-specific optimization for appearance and motion reconstruction, facilitating more nuanced behavioral modeling and quality-aware anomaly assessment. Experiments on three benchmark datasets demonstrate that M2S2L framework achieves 98.5%, 92.1%, and 77.9% frame-level AUCs on UCSD Ped2, CUHK Avenue, and ShanghaiTech respectively, while maintaining efficiency with 20.1G FLOPs and 45 FPS inference speed, making it suitable for practical surveillance deployment.