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LECLIP: Boosting Zero-Shot Anomaly Detection With Local Enhanced CLIP

文献类型：期刊论文

作者Liu, Yuyao4; Li, Qingyong3; Wang, Zhehong4; Kato, Jien1; Zhang, Jie2; Wang, Wen4
刊名IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
出版日期2025
卷号74页码:11
关键词Visualization Anomaly detection Training Accuracy Image representation Feature extraction Biomedical imaging Semantics Attention mechanisms Artificial intelligence Echo-attention (EA) local alignment (LA) module local enhanced CLIP (LECLIP) zero-shot anomaly detection (ZSAD)
ISSN号0018-9456
DOI10.1109/TIM.2025.3571124
英文摘要Zero-shot anomaly detection (ZSAD) is a critical task that detects anomalies without any training samples from the target application, which is crucial for applications in diverse fields such as industrial quality control and medical imaging analysis. Recent advances have seen the application of contrastive language-image pretraining (CLIP) in ZSAD, exploiting its robust visual-linguistic alignment and zero-shot learning capabilities. However, CLIP is primarily designed for natural image classification, emphasizing global visual embeddings, while anomaly detection (AD) requires a more accurate representation of anomalous regions and more precise local visual embeddings. To overcome these limitations, this article proposes the local enhanced CLIP (LECLIP) framework for ZSAD. LECLIP incorporates a local alignment (LA) module that divides images into blocks and aligns them with learnable text embeddings, ensuring precise relevance expression. Furthermore, a training-free echo-attention (EA) is proposed to complement the traditional QKV attention, enabling the model to capture both global and local image details effectively, thus providing a more accurate and detailed image representation. Experimental results show that LECLIP achieves superior performance on 15 challenging datasets, including six industrial datasets and nine medical datasets. Code is available at https://github.com/lyy70/LECLIP
资助项目Fundamental Research Funds for the Central Universities[2024QYBS026] ; Fundamental Research Funds for the Central Universities[2023JBZY037] ; Fundamental Research Funds for the Central Universities[2022JBMC055] ; Fundamental Research Funds for the Central Universities[2022JBQY007] ; Beijing Natural Science Foundation[L231019] ; National Natural Science Foundation of China[62276019] ; National Natural Science Foundation of China[62306028] ; Langfang Research and Development Projects[2023011003B] ; Shenzhen Science and Technology Program Project[KJZD20240903102742055]
WOS研究方向Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:001504194800024
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/42347]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Wen
作者单位1.Kochi Univ Technol, Sch Data & Innovat, Kochi 7828502, Japan
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China
3.Beijing Jiaotong Univ, Frontiers Sci Ctr Smart High Speed Railway Syst, Beijing 100044, Peoples R China
4.Beijing Jiaotong Univ, Key Lab Big Data & Artificial Intelligence Transpo, Minist Educ, Beijing 100044, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Yuyao,Li, Qingyong,Wang, Zhehong,et al. LECLIP: Boosting Zero-Shot Anomaly Detection With Local Enhanced CLIP[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2025,74:11.
APA Liu, Yuyao,Li, Qingyong,Wang, Zhehong,Kato, Jien,Zhang, Jie,&Wang, Wen.(2025).LECLIP: Boosting Zero-Shot Anomaly Detection With Local Enhanced CLIP.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,74,11.
MLA Liu, Yuyao,et al."LECLIP: Boosting Zero-Shot Anomaly Detection With Local Enhanced CLIP".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74(2025):11.

入库方式： OAI收割

来源：计算技术研究所

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