Zero-shot anomaly classification and segmentation (AC/AS) aim to detect anomalous samples and regions without any training data, a capability increasingly crucial in industrial inspection and medical imaging. This dissertation aims to investigate the core challenges of zero-shot AC/AS and presents principled solutions rooted in theory and algorithmic design. We first formalize the problem of consistent anomalies, a failure mode in which recurring similar anomalies systematically bias distance-based methods. By analyzing the statistical and geometric behavior of patch representations from pre-trained Vision Transformers, we identify two key phenomena - similarity scaling and neighbor-burnout - that describe how relationships among normal patches change with and without consistent anomalies in settings characterized by highly similar objects. We then introduce CoDeGraph, a graph-based framework for filtering consistent anomalies built on the similarity scaling and neighbor-burnout phenomena. Through multi-stage graph construction, community detection, and structured refinement, CoDeGraph effectively suppresses the influence of consistent anomalies. Next, we extend this framework to 3D medical imaging by proposing a training-free, computationally efficient volumetric tokenization strategy for MRI data. This enables a genuinely zero-shot 3D anomaly detection pipeline and shows that volumetric anomaly segmentation is achievable without any 3D training samples. Finally, we bridge batch-based and text-based zero-shot methods by demonstrating that CoDeGraph-derived pseudo-masks can supervise prompt-driven vision-language models. Together, this dissertation provides theoretical understanding and practical solutions for the zero-shot AC/AS problem.

翻译：零样本异常分类与分割（AC/AS）旨在无需任何训练数据的情况下检测异常样本及区域，这一能力在工业检测与医学影像领域日益关键。本论文旨在探究零样本AC/AS的核心挑战，并提出基于理论与算法设计的系统性解决方案。我们首先形式化定义了一致异常问题——这是一种基于距离的方法因反复出现的相似异常而产生系统性偏差的失效模式。通过分析预训练视觉Transformer（Vision Transformers）中图像块表征的统计与几何特性，我们识别出两个关键现象——相似度缩放与邻域衰减——它们描述了在高度相似物体的场景中，正常图像块间关系如何随一致异常的存在与否而变化。随后，我们提出了CoDeGraph，一个基于相似度缩放与邻域衰减现象构建的图结构框架，用于滤除一致异常。通过多阶段图构建、社区检测与结构化优化，CoDeGraph有效抑制了一致异常的影响。接着，我们将该框架扩展至三维医学影像，提出了一种无需训练、计算高效的MRI数据体素化标记策略。这实现了真正的零样本三维异常检测流程，并证明无需任何三维训练样本即可完成体素级异常分割。最后，我们通过实验证明CoDeGraph衍生的伪掩码可指导基于提示的视觉-语言模型，从而将基于批处理与基于文本的零样本方法相连接。综上，本论文为零样本AC/AS问题提供了理论理解与实践解决方案。