Vision-language models have recently shown strong generalization in zero-shot anomaly detection (ZSAD), enabling the detection of unseen anomalies without task-specific supervision. However, existing approaches typically rely on fixed textual prompts, which struggle to capture complex semantics, and focus solely on spatial-domain features, limiting their ability to detect subtle anomalies. To address these challenges, we propose a wavelet-enhanced mixture-of-experts prompt learning method for ZSAD. Specifically, a variational autoencoder is employed to model global semantic representations and integrate them into prompts to enhance adaptability to diverse anomaly patterns. Wavelet decomposition extracts multi-frequency image features that dynamically refine textual embeddings through cross-modal interactions. Furthermore, a semantic-aware mixture-of-experts module is introduced to aggregate contextual information. Extensive experiments on 14 industrial and medical datasets demonstrate the effectiveness of the proposed method.
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CASI Vision Technology CO., LTD.（中智视觉科技有限公司）；University of Technology Sydney（悉尼科技大学）；Hangzhou Dianzi University（杭州电子科技大学）；Tsinghua University（清华大学）
Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）
FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（西安光学精密机械研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）
MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Vision-Language Feature Alignment for Road Anomaly Segmentation
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑启发智能科学与技术研究院）
Visual anomaly classification and segmentation are vital for automating industrial quality inspection. The focus of prior research in the field has been on training custom models for each quality inspection task, which requires task-specific images and annotation. In this paper we move away from this regime, addressing zero-shot and few-normal-shot anomaly classification and segmentation. Recently CLIP, a vision-language model, has shown revolutionary generality with competitive zero-/few-shot performance in comparison to full-supervision. But CLIP falls short on anomaly classification and seg…
VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, China（光电信息采集与防护技术国家重点实验室，安徽大学，合肥，中国）；School of Artificial Intelligence, Anhui University, Hefei, China（人工智能学院，安徽大学，合肥，中国）；College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China（智能科学与技术学院，国防科技大学，长沙，中国）
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP
Xi’an Jiaotong University（西安交通大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Xiamen University（厦门大学）；Shenzhen University（深圳大学）
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（大数据分析与处理广东省重点实验室）
PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Harbin Engineering University（哈尔滨工程大学）；Key Laboratory of Advanced Marine Communication and Information Technology（先进海洋通信与信息技术重点实验室）；University of Toronto（多伦多大学）；Kanagawa University（神奈川大学）
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人机混合增强智能全国重点实验室与人工智能与机器人研究所）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Xiamen University（厦门大学）；Shenzhen University（深圳大学）
DevPrompt: Deviation-Based Prompt Learning for One-Normal ShotImage Anomaly Detection
Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）；Computer Research Institute of Montreal (CRIM)（蒙特利尔计算机研究 institute）
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
Corporate Research, Robert Bosch GmbH（罗伯特·博世集团企业研究部）；Otto-von-Guericke-University（奥托·冯·格里克大学）；Institute for Artificial Intelligence, University of Stuttgart（斯图加特大学人工智能研究所）；Faculty of Computer Science, UniVie Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学系）；University of Southampton（南安普顿大学）
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
College of Information and Engineering, Capital Normal University（首都师范大学信息工程学院）；Peking University Third Hospital（北京大学第三医院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC（中国科学技术大学生命科学与医学部生物医学工程学院）；MIRACLE Center, Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州高等研究院MIRACLE中心）；Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学国家重点实验室）；Stanford University（斯坦福大学）
Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention
organization= School of Aeronautics；Astronautics , addressline= Zhejiang University , city= Hangzhou , postcode= 310027 , country= China；organization= School of Computer Science；organization= Ningbo Innovation Center , addressline= Zhejiang University , city= Ningbo , country= China；organization= Department of Automation , addressline= Tsinghua University , city= Beijing , postcode= 100084 , country= China；organization= College of Information Science \& Electronic Engineering , addressline= Zhejiang University , city= Hangzhou , postcode= 310027 , country= China
MadCLIP: Few-shot Medical Anomaly Detection with CLIP
Department of Computer Science, University of Verona（威尼斯大学计算机科学系）；AI for Good (AIGO) Research Unit, Istituto Italiano di Tecnologia（意大利技术研究院人工智能为善研究单元）
GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
