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MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

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arxiv 2404.06564 v4 pith:UNB3AIBO submitted 2024-04-09 cs.CV

Haoyang He , Yuhu Bai , Jiangning Zhang , Qingdong He , Hongxu Chen , Zhenye Gan , Chengjie Wang , Xiangtai Li
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Guanzhong Tian Lei Xie
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classification cs.CV
keywords anomalydetectionspacestatelong-rangemambaadmodelsscanning
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring (Locality-Enhanced State Space) LSS modules at multi-scales. The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi-kernel convolutions operations, effectively captures both long-range and local information. The HSS block, utilizing (Hybrid Scanning) HS encoders, encodes feature maps into five scanning methods and eight directions, thereby strengthening global connections through the (State Space Model) SSM. The use of Hilbert scanning and eight directions significantly improves feature sequence modeling. Comprehensive experiments on six diverse anomaly detection datasets and seven metrics demonstrate state-of-the-art performance, substantiating the method's effectiveness. The code and models are available at https://lewandofskee.github.io/projects/MambaAD.

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Cited by 5 Pith papers

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  1. Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation

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    TopoTTA integrates persistent homology into test-time adaptation to derive topological pseudo-labels from anomaly maps, improving segmentation by an average 15% F1 on six benchmarks while generalizing across 2D and 3D data.

  2. AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization

    cs.CV 2025-08 conditional novelty 6.0 of 10

    AD-FM combines multi-stage reasoning with localization-aware rewards to fine-tune MLLMs for anomaly detection, improving average accuracy by about 22 percentage points over the base model.

  3. Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    FiSeCLIP achieves state-of-the-art zero-shot anomaly detection by using a batch of test images as mutual references and filtering noisy features with text-guided masks, without any training.

  4. State Space Models Meet Remote Sensing: A Survey

    cs.CV 2026-06 unverdicted novelty 2.0 of 10

    A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.

  5. A Survey of Mamba

    cs.LG 2024-08 unverdicted novelty 2.0 of 10

    The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.

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