Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2508.01591v1
• Date: Sun, 03 Aug 2025 05:07:38 GMT
• Title: Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection
• Authors: Hanxi Li, Jingqi Wu, Lin Yuanbo Wu, Mingliang Li, Deyin Liu, Jialie Shen, Chunhua Shen,
• Abstract summary: Self-Navigated Residual Mamba (SNARM) is a novel framework for universal industrial anomaly detection.<n> SNARM iteratively refines anomaly detection by comparing test patches against adaptively selected in-image references.<n>Experiments on MVTec AD, MVTec 3D, and VisA benchmarks demonstrate that SNARM achieves state-of-the-art (SOTA) performance.
• Score: 42.42739543127113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within test images to enhance anomaly discrimination. Unlike conventional methods that depend solely on pre-trained features from normal training data, SNARM dynamically refines anomaly detection by iteratively comparing test patches against adaptively selected in-image references. Specifically, we first compute the ``inter-residuals'' features by contrasting test image patches with the training feature bank. Patches exhibiting small-norm residuals (indicating high normality) are then utilized as self-generated reference patches to compute ``intra-residuals'', amplifying discriminative signals. These inter- and intra-residual features are concatenated and fed into a novel Mamba module with multiple heads, which are dynamically navigated by residual properties to focus on anomalous regions. Finally, AD results are obtained by aggregating the outputs of a self-navigated Mamba in an ensemble learning paradigm. Extensive experiments on MVTec AD, MVTec 3D, and VisA benchmarks demonstrate that SNARM achieves state-of-the-art (SOTA) performance, with notable improvements in all metrics, including Image-AUROC, Pixel-AURC, PRO, and AP.

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