English(EN) CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis

CORE框架使用LMM进行工业环境中的异常合成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了CORE，一个利用大型多模态模型（LMM）在工业环境中进行无训练异常合成的新框架。该方法通过一种提示优化技术解决了异常样本稀缺的问题，该技术将异常生成与保留正常区域分离开来。CORE通过自监督的对比与反思机制来优化提示，使LMM能够在零样本和单样本场景下生成逼真的异常，而无需进行任务特定的训练。在MVTec-AD和VisA数据集上的实验表明，CORE显著提高了下游异常检测性能，并且可以与各种LMM集成。 AI

影响通过利用现有的LMM生成更逼真的合成异常，增强了工业异常检测能力。

排序理由详细介绍使用LMM进行异常合成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Haoxiang Rao, Fang Zhao, Zhao Wang, Weijia Li, Chenyang Si, Caifeng Shan · 2026-10-15 04:00

CORE：用于异常合成中自监督提示优化的对比与反思

arXiv:2610.19610v1 Announce Type: new Abstract: Industrial anomaly detection (AD) is bottlenecked by the scarcity of anomalous samples, making high-fidelity anomaly synthesis crucial for improving downstream performance. However, existing methods either rely on scarce real anomal…

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CORE：用于异常合成中自监督提示优化的对比与反思

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