Vision-Language Models (VLMs), particularly CLIP, have revolutionized anomaly detection by enabling zero-shot and few-shot defect identification without extensive labeled datasets. By learning aligned representations of images and text, VLMs facilitate anomaly classification and segmentation through natural language descriptions of normal and abnormal states, eliminating traditional requirements for task-specific training or defect examples. This project presents a comprehensive analysis of VLM-based approaches for anomaly classification (AC) and anomaly segmentation (AS). We systematically investigate key architectural paradigms including sliding window-based dense feature extraction (WinCLIP), multi-stage feature alignment with learnable projections (AprilLab framework), and compositional prompt ensemble strategies. Our analysis evaluates these methods across critical dimensions: feature extraction mechanisms, text-visual alignment strategies, prompt engineering techniques, zero-shot versus few-shot trade-offs, computational efficiency, and cross-domain generalization. Through rigorous experimentation on benchmarks such as MVTec AD and VisA, we compare classification accuracy, segmentation precision, and inference efficiency. The primary contribution is a foundational understanding of how and why VLMs succeed in anomaly detection, synthesizing practical insights for method selection and identifying current limitations. This work aims to facilitate informed adoption of VLM-based methods in industrial quality control and guide future research directions.

翻译：视觉语言模型（VLMs），特别是CLIP，通过实现无需大量标注数据集的零样本与少样本缺陷识别，彻底改变了异常检测领域。通过学习图像与文本的对齐表征，VLMs能够借助对正常与异常状态的自然语言描述，促进异常分类与分割，从而消除了传统方法对任务特定训练或缺陷样本的需求。本项目对基于VLM的异常分类（AC）与异常分割（AS）方法进行了全面分析。我们系统地研究了关键架构范式，包括基于滑动窗口的密集特征提取（WinCLIP）、具有可学习投影的多阶段特征对齐（AprilLab框架）以及组合式提示词集成策略。我们的分析从多个关键维度评估这些方法：特征提取机制、文本-视觉对齐策略、提示工程技术、零样本与少样本的权衡、计算效率以及跨领域泛化能力。通过在MVTec AD和VisA等基准数据集上的严格实验，我们比较了分类准确率、分割精度和推理效率。本研究的主要贡献在于建立了关于VLM为何及如何在异常检测中取得成功的基础性理解，综合了方法选择的实用见解并指出了当前局限。本工作旨在促进基于VLM的方法在工业质量控制中的明智应用，并为未来研究方向提供指引。