PatchEAD: 统一工业视觉提示框架用于补丁专属异常检测
PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection
AI总结：
PatchEAD提出统一的补丁聚焦框架，实现无需训练的工业异常检测，兼容多种基础模型并提升补丁相似性鲁棒性。
摘要：
工业异常检测日益依赖于基础模型，旨在实现强大的分布外泛化能力和快速适应现实世界部署。值得注意的是，过去的研究主要集中在文本提示调优上，留下内在视觉对应关系被分割成每个基础模型特定的处理步骤。我们旨在通过提出一个统一的补丁聚焦框架，即补丁专属异常检测（PatchEAD），来解决这一限制，实现无需训练的异常检测，该方法兼容多种基础模型。该框架构建了视觉提示技术，包括对齐模块和前景遮蔽。我们的实验显示，与先前工作相比，在少样本和批量零样本情况下表现更优，尽管没有文本特征。我们的研究进一步考察了骨干结构和预训练特性如何影响补丁相似性鲁棒性，为选择和配置基础模型提供可行指导。这些结果证实，一个良好的统一补丁-only框架可以在无需精心设计文本提示的情况下实现快速、校准轻量的部署。
英文摘要：
Industrial anomaly detection is increasingly relying on foundation models, aiming for strong out-of-distribution generalization and rapid adaptation in real-world deployments. Notably, past studies have primarily focused on textual prompt tuning, leaving the intrinsic visual counterpart fragmented into processing steps specific to each foundation model. We aim to address this limitation by proposing a unified patch-focused framework, Patch-Exclusive Anomaly Detection (PatchEAD), enabling training-free anomaly detection that is compatible with diverse foundation models. The framework constructs visual prompting techniques, including an alignment module and foreground masking. Our experiments show superior few-shot and batch zero-shot performance compared to prior work, despite the absence of textual features. Our study further examines how backbone structure and pretrained characteristics affect patch-similarity robustness, providing actionable guidance for selecting and configuring foundation models for real-world visual inspection. These results confirm that a well-unified patch-only framework can enable quick, calibration-light deployment without the need for carefully engineered textual prompts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
