无需训练，用逻辑与局部搜索结合实现可追溯的工业缺陷检测。
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection
- 构建视觉-逻辑地图，整合正常样本与规范信息作为参考。
- 全局流提取可验证视觉事实，局部流在预算内搜索关键区域证据。
- 适合缺乏缺陷样本的早期部署场景，决策全程可追溯。
大型多模态模型虽具备强少样本泛化能力，但工业缺陷检测仍面临挑战：缺陷尺寸小、输入分辨率有限，且文本标准常与视觉证据脱节。现有基于优化的方法虽能提升对齐效果，但通常需大量缺陷样本，难以在早期部署阶段应用。本文提出无需训练的GLLS框架，实现参考引导的多模态上下文验证。GLLS利用部件感知的视觉-逻辑图谱，在推理上下文中组织正常参考与结构化规范。其融合全局逻辑流（由SAM 3提取部分可验证视觉事实）与细粒度动作流（在固定预算内通过MCTS选择局部证据区域）。在MMAD-QA及其他异常检测数据集上的实验表明，该方法持续优于匹配基线与通用基线，同时保证诊断决策始终可追溯至明确的视觉证据。
原文摘要 · Abstract (English)
Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and textual standards are not always grounded in visual evidence. Recent optimization-based methods improve alignment through fine-tuning, but they often require many defective samples, which are unavailable in early deployment. We present Global Logic and Local Search (GLLS), a training-free framework for reference-guided multimodal in-context verification. GLLS uses a Part-Aware Visual-Logical Atlas to organize normal references and structured specifications in the inference context. It combines a Global & Logic Stream, where SAM 3 extracts partially checkable visual facts, with a Fine-Grained & Actions Stream, where MCTS selects local evidence crops under a fixed budget. Experiments on MMAD-QA and additional anomaly detection datasets show consistent gains over matched and general-purpose baselines, while keeping the final diagnostic decision traceable to explicit visual evidence throughout the inspection trace.
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