# VADMamba++: Efficient Video Anomaly Detection via Hybrid Modeling in Grayscale Space

arXiv: https://arxiv.org/abs/2604.00360 · HTML: https://mlsanity.com/paper/2604.00360/

## 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 28 of its 69 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## 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, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision](https://mlsanity.com/paper/2007.04687/) (2020)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2404.06564/) (2024)
- [Normalizing Flows for Human Pose Anomaly Detection](https://mlsanity.com/paper/2211.10946/) (2022)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Video Event Restoration Based on Keyframes for Video Anomaly Detection](https://mlsanity.com/paper/2304.05112/) (2023)
- [Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors](https://mlsanity.com/paper/2306.12041/) (2023)
- [Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection](https://mlsanity.com/paper/2209.12148/) (2022)
- [Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection](https://mlsanity.com/paper/2207.10948/) (2022)
- [MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection](https://mlsanity.com/paper/2403.14497/) (2024)
- [Real-Time Anomaly Detection and Localization in Crowded Scenes](https://mlsanity.com/paper/1511.06936/) (2015)
- [Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection](https://mlsanity.com/paper/2209.02899/) (2022)
- [Learnable Locality-Sensitive Hashing for Video Anomaly Detection](https://mlsanity.com/paper/2111.07839/) (2021)
- [Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model](https://mlsanity.com/paper/2412.09026/) (2024)
- [STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection](https://mlsanity.com/paper/2412.20084/) (2024)
- [VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection](https://mlsanity.com/paper/2409.16225/) (2024)
- [VADMamba: Exploring State Space Models for Fast Video Anomaly Detection](https://mlsanity.com/paper/2503.21169/) (2025)
- [Automatic Controllable Colorization via Imagination](https://mlsanity.com/paper/2404.05661/) (2024)
- [DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection](https://mlsanity.com/paper/2406.02976/) (2024)
- [Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention](https://mlsanity.com/paper/2407.15424/) (2024)
- [Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection](https://mlsanity.com/paper/2409.17608/) (2024)

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

- [Strictly Causal Streaming Video Anomaly Detection with a Theoretically-Grounded State-Space Core](https://mlsanity.com/paper/2608.24810/) (2026)

## Abstract (arXiv)

VADMamba pioneered the introduction of Mamba to Video Anomaly Detection (VAD), achieving high accuracy and fast inference through hybrid proxy tasks. Nevertheless, its heavy reliance on optical flow as auxiliary input and inter-task fusion scoring constrains its applicability to a single proxy task. In this paper, we introduce VADMamba++, an efficient VAD method based on the Gray-to-RGB paradigm that enforces a Single-Channel to Three-Channel reconstruction mapping, designed for a single proxy task and operating without auxiliary inputs. This paradigm compels inferring color appearances from grayscale structures, allowing anomalies to be more effectively revealed through dual inconsistencies between structure and chromatic cues. Specifically, VADMamba++ reconstructs grayscale frames into the RGB space to simultaneously discriminate structural geometry and chromatic fidelity, thereby enhancing sensitivity to explicit visual anomalies. We further design a hybrid modeling backbone that integrates Mamba, CNN, and Transformer modules to capture diverse normal patterns while suppressing the appearance of anomalies. Furthermore, an intra-task fusion scoring strategy integrates explicit future-frame prediction errors with implicit quantized feature errors, further improving accuracy under a single task setting. Extensive experiments on three benchmark datasets demonstrate that VADMamba++ outperforms state-of-the-art methods while meeting performance and efficiency, especially under a strict single-task setting with only frame-level inputs.