通过残差学习从查询样本中搜寻正常性以实现通用异常检测
Hunting Normality from Query Sample via Residual Learning for Generalist Anomaly Detection
将残差特征从「直接建模分布」改为「引导学习实例级正常性代理」，并用支持集特征动态聚合查询相关的正常性代理，这一思路可迁移到少样本缺陷检测、开放类别异常定位以及任何需要类不变表征的定位任务。
原文依据 re-purposes residuals as a guide to learn instance-level normality, rather than modeling their distribution directly
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论通用异常检测（GAD）旨在通过训练一个能够泛化到未见类别的统一模型，克服传统异常检测受限于特定领域的问题。一种有前景的 GAD 策略是利用残差特征构建类不变空间。然而，现有直接对残差分布建模的方法面临不可预测的风险：残差特征与实例特征之间存在不一致性，即细微缺陷可能产生较小的残差（假阴性），或者由于正常性本身的多样性，正常特征的残差可能较大（假阳性）。为解决这些局限，我们提出一种新的基于残差的学习框架，将残差重新用作学习实例级正常性的引导，而非直接对其分布建模。我们的框架包含两个新的基于注意力的模块：残差特征学习（RFL）模块，利用可学习代理从残差特征中捕获多样模式；以及支持集正常性学习（NLS）模块，利用这些残差代理从支持实例特征中聚合与查询相关的正常性代理。这些动态生成的正常性代理随后被用于在查询图像块特征中搜寻正常性，从而实现准确的异常定位。在 GAD 基准上的大量实验证明了本方法的有效性。代码见 HNQ-GAD。
英文原文摘要 1417 字符
Generalist Anomaly Detection (GAD) seeks to overcome the domain-specific limitations of traditional anomaly detection by training a unified model that can generalize to unseen classes. A promising GAD strategy involves using residual features to create a class-invariant space. However, existing methods that directly model the distribution of residuals face unpredictable risks: there is inconsistency between residual and instance features, i.e., subtle defects may yield small residuals (false negatives), or normal feature residuals could be large due to the diversity of normality (false positives). To address these limitations, we propose a novel residual-based learning framework that re-purposes residuals as a guide to learn instance-level normality, rather than modeling their distribution directly. Our framework features two new attention-based modules: Residual Feature Learning (RFL), which uses learnable proxies to capture diverse patterns from the residual features, and Normality Learning from Support (NLS), which leverages these residual proxies to aggregate query-related normality proxies from the support instance features. These dynamically generated normality proxies are then used to hunt for normality within the query patch features, enabling accurate anomaly localization. Extensive experiments on GAD benchmarks demonstrate the effectiveness of our method. Code is available at HNQ-GAD.