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Updated: Jun 6, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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重新考虑可学习的细粒度文本提示,用于在视觉语言模型中检测少数镜头异常

Delong Han1, Luo Xu1, Mingle Zhou1

  • 1Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China; Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, 250014, China.

Neural networks : the official journal of the International Neural Network Society
|November 24, 2024
PubMed
概括

本研究引入了在工业环境中用于少数拍摄异常检测 (FSAD) 的新框架,利用细粒度可学习的文本提示. 该方法提高了准确性和概括性,在有限的数据中识别工业缺陷.

关键词:
几次射击异常检测检测细粒度的文本提示提示.工业异常检测检测 工业异常检测预先训练有素的视觉语言模型

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