Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection

Chaoqin Huang; Aofan Jiang; Ya Zhang; Yanfeng Wang

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arXiv · 2308.04789

Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection

Chaoqin Huang·Aofan Jiang·Ya Zhang·Yanfeng Wang

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Abstract

Anomaly detection has gained considerable attention due to its broad range of applications, particularly in industrial defect detection. To address the challenges of data collection, researchers have introduced zero-/few-shot anomaly detection techniques that require minimal normal images for each category. However, complex industrial scenarios often involve multiple objects, presenting a significant challenge. In light of this, we propose a straightforward yet powerful multi-scale memory comparison framework for zero-/few-shot anomaly detection. Our approach employs a global memory bank to capture features across the entire image, while an individual memory bank focuses on simplified scenes containing a single object. The efficacy of our method is validated by its remarkable achievement of 4th place in the zero-shot track and 2nd place in the few-shot track of the Visual Anomaly and Novelty Detection (VAND) competition.

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BibTeX RIS

Chaoqin Huang, Aofan Jiang, Ya Zhang, Yanfeng Wang. 2023-08-09. Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection. https://arxiv.org/abs/2308.04789

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