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

• URL: http://arxiv.org/abs/2308.04789v2
• Date: Tue, 2 Jan 2024 04:39:09 GMT
• Title: Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection
• Authors: Chaoqin Huang, Aofan Jiang, Ya Zhang, Yanfeng Wang
• Abstract summary: 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. We propose a straightforward yet powerful multi-scale memory comparison framework for zero-/few-shot anomaly detection.
• Score: 35.76765622970398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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