解决长尾分布下在线异常检测难题，无需类别标签也能精准识别缺陷。
Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts
- 提出无类别感知的框架，避免依赖无法获取的类别标签。
- 在MVTec上比现有方法提升4.63%图像AUROC，长尾在线设置提升0.53%。
- 适用于工业制造与医疗领域，适合真实场景中无标签异常检测。
异常检测（AD）旨在识别图像中的缺陷区域。近期研究聚焦于无需异常样本、长尾分布训练数据以及统一模型支持所有类别的AD。此外，在线AD学习也受到关注。本文拓展至更现实的场景，提出长尾在线异常检测（LTOAD）新任务。我们发现，现有离线长尾AD方法无法直接用于在线设置，因其依赖类别标签，而在线场景中不可用。为此，我们设计了一种无类别感知的长尾AD框架，并适配在线学习。实验表明，该方法在多数离线长尾AD设置中优于现有最优基线，涵盖工业制造与医疗领域。尤其在MVTec上，相比有类别标签且知悉类别数的方法，图像AUROC提升4.63%；在最具挑战性的长尾在线设置中，相较基线提升0.53%。相关基准已发布：https://doi.org/10.5281/zenodo.16283852。
原文摘要 · Abstract (English)
Anomaly detection (AD) identifies the defect regions of a given image. Recent works have studied AD, focusing on learning AD without abnormal images, with long-tailed distributed training data, and using a unified model for all classes. In addition, online AD learning has also been explored. In this work, we expand in both directions to a realistic setting by considering the novel task of long-tailed online AD (LTOAD). We first identified that the offline state-of-the-art LTAD methods cannot be directly applied to the online setting. Specifically, LTAD is class-aware, requiring class labels that are not available in the online setting. To address this challenge, we propose a class-agnostic framework for LTAD and then adapt it to our online learning setting. Our method outperforms the SOTA baselines in most offline LTAD settings, including both the industrial manufacturing and the medical domain. In particular, we observe +4.63% image-AUROC on MVTec even compared to methods that have access to class labels and the number of classes. In the most challenging long-tailed online setting, we achieve +0.53% image-AUROC compared to baselines. Our LTOAD benchmark is released here: https://doi.org/10.5281/zenodo.16283852 .
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