多模态大语言模型能否被引导以改进工业异常检测？
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
AI总结：
提出多专家框架Echo，通过集成参考提取、知识引导、推理和决策模块增强多模态大语言模型在工业异常检测中的领域适应性与精确度。
摘要：
在工业环境中，准确检测异常对于维持产品质量和确保运行安全至关重要。传统的工业异常检测（IAD）模型通常在灵活性和适应性方面存在困难，尤其是在新缺陷类型和操作变化频繁出现的动态生产环境中。多模态大语言模型（MLLMs）的最新进展通过结合视觉和文本信息处理能力，有望克服这些局限性。由于在大型、多样化数据集上接受训练，MLLMs在通用视觉理解方面表现出色，但它们缺乏领域特定知识，例如行业特定的缺陷容忍度，这限制了它们在IAD任务中的有效性。为了应对这些挑战，我们提出了Echo，一种新颖的多专家框架，旨在提升MLLM在IAD中的性能。Echo集成了四个专家模块：Reference Extractor通过检索相似的正常图像提供上下文基线；Knowledge Guide提供领域特定见解；Reasoning Expert能够针对复杂查询进行结构化、逐步的推理；Decision Maker综合所有模块的信息，提供精确且具有上下文感知能力的响应。在MMAD基准上进行评估，Echo在适应性、精确性和鲁棒性方面显示出显著改进，更接近满足现实世界工业异常检测的需求。
英文摘要：
In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptability, especially in dynamic production environments where new defect types and operational changes frequently arise. Recent advancements in Multimodal Large Language Models (MLLMs) hold promise for overcoming these limitations by combining visual and textual information processing capabilities. MLLMs excel in general visual understanding due to their training on large, diverse datasets, but they lack domain-specific knowledge, such as industry-specific defect tolerance levels, which limits their effectiveness in IAD tasks. To address these challenges, we propose Echo, a novel multi-expert framework designed to enhance MLLM performance for IAD. Echo integrates four expert modules: Reference Extractor which provides a contextual baseline by retrieving similar normal images, Knowledge Guide which supplies domain-specific insights, Reasoning Expert which enables structured, stepwise reasoning for complex queries, and Decision Maker which synthesizes information from all modules to deliver precise, context-aware responses. Evaluated on the MMAD benchmark, Echo demonstrates significant improvements in adaptability, precision, and robustness, moving closer to meeting the demands of real-world industrial anomaly detection.
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。
