Search is All You Need for Few-shot Anomaly Detection

• URL: http://arxiv.org/abs/2504.11895v1
• Date: Wed, 16 Apr 2025 09:21:34 GMT
• Title: Search is All You Need for Few-shot Anomaly Detection
• Authors: Qishan Wang, Jia Guo, Shuyong Gao, Haofen Wang, Li Xiong, Junjie Hu, Hanqi Guo, Wenqiang Zhang,
• Abstract summary: Few-shot anomaly detection (FSAD) has emerged as a crucial yet challenging task in industrial inspection.<n>We show that a straightforward nearest-neighbor search framework can surpass state-of-the-art performance in both single-class and multi-class FSAD scenarios.<n>Our method achieves remarkable image-level AUROC scores of 97.4%, 94.8%, and 70.8% respectively.
• Score: 39.737510049667556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot anomaly detection (FSAD) has emerged as a crucial yet challenging task in industrial inspection, where normal distribution modeling must be accomplished with only a few normal images. While existing approaches typically employ multi-modal foundation models combining language and vision modalities for prompt-guided anomaly detection, these methods often demand sophisticated prompt engineering and extensive manual tuning. In this paper, we demonstrate that a straightforward nearest-neighbor search framework can surpass state-of-the-art performance in both single-class and multi-class FSAD scenarios. Our proposed method, VisionAD, consists of four simple yet essential components: (1) scalable vision foundation models that extract universal and discriminative features; (2) dual augmentation strategies - support augmentation to enhance feature matching adaptability and query augmentation to address the oversights of single-view prediction; (3) multi-layer feature integration that captures both low-frequency global context and high-frequency local details with minimal computational overhead; and (4) a class-aware visual memory bank enabling efficient one-for-all multi-class detection. Extensive evaluations across MVTec-AD, VisA, and Real-IAD benchmarks demonstrate VisionAD's exceptional performance. Using only 1 normal images as support, our method achieves remarkable image-level AUROC scores of 97.4%, 94.8%, and 70.8% respectively, outperforming current state-of-the-art approaches by significant margins (+1.6%, +3.2%, and +1.4%). The training-free nature and superior few-shot capabilities of VisionAD make it particularly appealing for real-world applications where samples are scarce or expensive to obtain. Code is available at https://github.com/Qiqigeww/VisionAD.

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