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.

提出了一个简单而强大的多尺度记忆对比框架用于零 / 少样本异常检测，在 Visual Anomaly and Novelty Detection (VAND) 竞赛中取得了零样本组第四名和少样本组第二名的显著成果。

多尺度记忆比较在零 / 少样本异常检测中的应用

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

The field of industrial defect detection using machine learning and deep
learning is a subject of active research. Datasets, also called benchmarks, are
used to compare and assess research results. There is a number of datasets in
industrial visual inspection, of varying quality. Thus, it is a difficult task
to determine which dataset to use. Generally speaking, datasets which include a
testing set, with precise labeling and made in real-world conditions should be
preferred. We propose a study of existing benchmarks to compare and expose
their characteristics and their use-cases. A study of industrial metrics
requirements, as well as testing procedures, will be presented and applied to
the studied benchmarks. We discuss our findings by examining the current state
of benchmarks for industrial visual inspection, and by exposing guidelines on
the usage of benchmarks.

对现有工业视觉检测基准进行研究，比较和揭示其特征和使用情况，并通过检查工业视觉检测基准的当前状态，揭示基准使用的指导方针。