WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Open accessAbstract
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 segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a compositional ensemble on state words and prompt templates and (2) efficient extraction and aggregation of window/patch/image-level features aligned with text. We also propose its few-normal-shot extension Win-CLIP+, which uses complementary information from normal images. In MVTec-AD (and VisA), without further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AU-ROC in zero-shot anomaly classification and segmentation while WinCLIP + does 93.1%/95.2% (83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins.
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision
Wang, Q., Wang, M., Sun, J., Chen, D., & Shi, P. (2025) · IEEE Access · doi:10.1109/access.2025.3571297 · Cited by 53 · see citing works
MediCLIP: Adapting CLIP for Few-Shot Medical Image Anomaly Detection
Zhang, X., Xu, M., Qiu, D., Yan, R., Lang, N., & Zhou, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_43 · Cited by 41 · see citing works
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Deep learning based infrared small object segmentation: Challenges and future directions
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UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., & Wang, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01415 · Cited by 37 · see citing works
A survey on RGB, 3D, and multimodal approaches for unsupervised industrial image anomaly detection
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Knowledge-Enhanced Dual-Stream Zero-Shot Composed Image Retrieval
Suo, Y., Ma, F., Zhu, L., & Yang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02545 · Cited by 34 · see citing works
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Zhu, J., Cai, S., Deng, F. Y., Ooi, B. C., & Wu, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681190 · Cited by 38 · see citing works
Multi-Scale CNN-Transformer Hybrid Network for Rail Fastener Defect Detection
He, J., Wang, W., Lv, F., Luo, H., Zhang, G., & Chen, Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3540846 · Cited by 33 · see citing works
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection
Heckler-Kram, L., Neudeck, J.-H., Scheler, U., König, R., & Steger, C. T. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02743-0 · Cited by 32 · see citing works
Yang, H., Hu, K., Wang, H., Hong, W., Wang, X., Wang, H., Song, Y., & Liu, Z. (2026) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2026.3689522 · Cited by 32 · see citing works
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
Qu, Z., Tao, X., Gong, X., Qu, S., Chen, Q., Zhang, Z., Wang, X., & Ding, G. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02830 · Cited by 27 · see citing works
Small Object Few-Shot Segmentation for Vision-Based Industrial Inspection
Zhang, Z., Niu, C., Zhao, Z., Zhang, X., & Chen, X. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3538070 · Cited by 25 · see citing works
Low-Shot Unsupervised Visual Anomaly Detection via Sparse Feature Representation
Zhang, F., Zhu, H., Cen, Y., Kan, S., Zhang, L., Vadakkepat, P., & Lee, T. H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3420818 · Cited by 23 · see citing works
Unseen Visual Anomaly Generation
Sun, H., Cao, Y., Dong, H., & Fink, O. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02375 · Cited by 23 · see citing works
GPT-4V-AD: Exploring Grounding Potential of VQA-Oriented GPT-4V for Zero-Shot Anomaly Detection
Zhang, J., He, H., Chen, X., Xue, Z., Wang, Y., Wang, C., Xie, L., & Liu, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9003-6_1 · Cited by 23 · see citing works
Anomaly detection for industrial applications: challenges, solutions, and future directions
Alzarooni, A., Iqbal, E., Khan, S. U., Javed, S., Moyo, B., & Abdulrahman, Y. (2025) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2025.2599548 · Cited by 29 · see citing works
Wang, T., Zhang, B., Jiang, D., & Li, D. (2025) · Sensors · doi:10.3390/s25103072 · Cited by 27 · see citing works
MCAD: Multi-classification anomaly detection with relational knowledge distillation
Li, Z., Ge, Y., Yue, X., & Meng, L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09838-0 · Cited by 21 · see citing works
CrackCLIP: Adapting Vision-Language Models for Weakly Supervised Crack Segmentation
Liang, F., Li, Q., Yu, H., & Wang, W. (2025) · Entropy · doi:10.3390/e27020127 · Cited by 20 · see citing works
Coating defect detection in intelligent manufacturing: Advances, challenges, and future trends
Zi, B., Tang, K. W., Li, Y., Feng, K., Liu, Y., & Wang, L. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103079 · Cited by 20 · see citing works
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising
Wang, C., Zhu, H., Peng, J., Wang, Y., Yi, R., Wu, Y., Ma, L., & Zhang, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3592089 · Cited by 20 · see citing works
MSAttnFlow: Normalizing flow for unsupervised anomaly detection with multi-scale attention
Hu, Z., Zeng, X., Li, Y., Yin, Z., Meng, E., Wei, Z., Zhu, L., & Wang, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111220 · Cited by 19 · see citing works
Foundation models and Transformers for anomaly detection: A survey
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VMAD: Visual-Enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
Deng, H., Luo, H., Zhai, W., Guo, Y., Cao, Y. H., & Kang, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3591656 · Cited by 18 · see citing works
Real-IAD D 3 : A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
Zhu, W., Wang, L., Zhou, Z., Wang, C., Pan, Y., Zhang, R., Chen, Z., Cheng, L., Gao, B.-B., Zhang, J., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01417 · Cited by 18 · see citing works
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
Fang, Q., Lv, W., & Su, Q. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755635 · Cited by 18 · see citing works
Dual-path Frequency Discriminators for few-shot anomaly detection
Bai, Y., Zhang, J., Chen, Z., Dong, Y., Cao, Y., & Tian, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112397 · Cited by 17 · see citing works
Long-Tailed Anomaly Detection with Learnable Class Names
Ho, C.-H., Peng, K., & Vasconcelos, N. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01182 · Cited by 17 · see citing works
DiffUT: Diffusion-based augmentation for limited ultrasonic testing defects in high-speed rail
Zhang, Q., Tian, K., Zhang, F., Li, J., Yang, K., Luo, L., Gao, X., & Peng, J. (2025) · NDT & E International · doi:10.1016/j.ndteint.2025.103388 · Cited by 16 · see citing works
Towards Training-free Anomaly Detection with Vision and Language Foundation Models
Zhang, J., Wang, G., Jin, Y., & Huang, D. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01416 · Cited by 16 · see citing works
MoEAD: A Parameter-Efficient Model for Multi-class Anomaly Detection
Meng, S., Meng, W., Zhou, Q., Li, S., Hou, W., & He, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73013-9_20 · Cited by 15 · see citing works
A survey on industrial image anomaly detection: methods, benchmarks and rethinks
Yu, M., Chen, Z., Liu, Y., Dong, C., & Song, K. (2025) · Measurement · doi:10.1016/j.measurement.2025.118377 · Cited by 18 · see citing works
EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models
Zhang, Z., Ruan, J., Gao, X., Liu, T., & Fu, Y. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209158 · Cited by 15 · see citing works
A Survey on Safe Multi-Modal Learning Systems
Zhao, T., Zhang, L., Ma, Y., & Cheng, L. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671462 · Cited by 15 · see citing works
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt
Gao, B.-B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_26 · Cited by 14 · see citing works
Jiang, J., He, Z., Wan, A., AL-Bukhaiti, K., Wang, K., Zhu, P., & Cheng, X. (2025) · Electronics · doi:10.3390/electronics14244785 · Cited by 14 · see citing works
Self-Prompt Mechanism for Few-Shot Image Recognition
Song, M., Wang, H., & Zhong, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28297 · Cited by 14 · see citing works
Open-World Semantic Segmentation Including Class Similarity
Sodano, M., Magistri, F., Nunes, L., Behley, J., & Stachniss, C. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00307 · Cited by 13 · see citing works
Few-Shot Defect Image Generation Based on Consistency Modeling
Shi, Q., Wei, J., Shen, F., & Zhang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73116-7_21 · Cited by 14 · see citing works
Wang, H., Peng, R., Zhu, Y., Li, J., Zhou, M., Ying, M., & Wang, S. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3563535 · Cited by 13 · see citing works
Li, Y., Yuan, S., Wang, H., Li, Q., Liu, M., Chen, X., Shi, G., & Zuo, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02035 · Cited by 11 · see citing works
An, S., Kim, J., Kim, S., Chikontwe, P., Jung, J., Jeon, H., & Park, S. H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124890 · Cited by 11 · see citing works
SimCLIP: Refining Image-Text Alignment with Simple Prompts for Zero-/Few-shot Anomaly Detection
Deng, C., Xu, H., Chen, X., Xu, H., Tu, X., Ding, X., & Huang, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681376 · Cited by 12 · see citing works
Li, J., Wen, S., & Fang, B. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3552986 · Cited by 11 · see citing works
Belton, N., Lawlor, A., & Curran, K. M. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103138 · Cited by 11 · see citing works
High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis
Ardelean, A.-T., & Weyrich, T. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00117 · Cited by 11 · see citing works
AnomalySD: One-for-all few-shot anomaly detection via pre-trained diffusion models
Yan, Z., Fang, Q., Lv, W., & Su, Q. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104668 · Cited by 10 · see citing works
Few-shot anomaly detection with adaptive feature transformation and descriptor construction
Zhengnan, H. U., Xiangrui, Z. E. N. G., Li, Y., Yin, Z., Meng, E., Zhu, L., & Kong, X. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.06.007 · Cited by 9 · see citing works
RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection
Cheng, Y., Cao, Y., Chen, R., & Shen, W. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711763 · Cited by 9 · see citing works
Multimodal fusion and knowledge distillation for improved anomaly detection
Lu, M., Chai, Y., Xu, K., Chen, W., Ao, F., & Ji, W. (2024) · The Visual Computer · doi:10.1007/s00371-024-03723-6 · Cited by 9 · see citing works
IAD-CLIP: Vision-Language Models for Zero-Shot Industrial Anomaly Detection
Li, Z., Ge, Y., Li, Q., & Meng, L. (2024) · International Conference on Advanced Mechatronic Systems · doi:10.1109/icamechs63130.2024.10818831 · Cited by 9 · see citing works
Lei, K., & Qi, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3534228 · Cited by 9 · see citing works
Li, C., Zhou, S., Kong, J., Qi, L., & Xue, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888834 · Cited by 9 · see citing works
Diffusion-based vision-language model for zero-shot anomaly detection in medical images
Chen, Y., Tao, H., Yang, Z., Cao, Y., Jiang, C., Hu, L., Xiong, P., & Qiu, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112181 · Cited by 9 · see citing works
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A Novel MAE-Based Self-Supervised Anomaly Detection and Localization Method
Chen, Y., Peng, H., Huang, L., Zhang, J., & Jiang, W. (2023) · IEEE Access · doi:10.1109/access.2023.3332475 · Cited by 10 · see citing works
MedicalCLIP: Anomaly-Detection Domain Generalization with Asymmetric Constraints
Hua, L., Luo, Y., Qi, Q., & Long, J. (2024) · Biomolecules · doi:10.3390/biom14050590 · Cited by 9 · see citing works
Anomaly detection and segmentation in industrial images using multi-scale reverse distillation
Liu, C., & Chung, C.-C. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112502 · Cited by 8 · see citing works
DC-AD: A Divide-and-Conquer Method for Few-Shot Anomaly Detection
Zhang, J., Yang, Z., & Song, Y. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111360 · Cited by 8 · see citing works
Jin, Q., Jiang, Y., Lu, X., Liu, Y., Chen, Y., Gao, D., Sun, Q. L., & Zhuo, C. (2024) · doi:10.1145/3676536.3676752 · Cited by 8 · see citing works
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
Huang, Z., Li, X., Liu, H., Xue, F., Wang, Y., & Zhou, Y. P. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00448 · Cited by 8 · see citing works
SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment
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Toward Zero-Shot Point Cloud Anomaly Detection: A Multiview Projection Framework
Cheng, Y., Cao, Y., Xie, G., Lu, Z., & Shen, W. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3648581 · Cited by 8 · see citing works
Visual defect obfuscation based self-supervised anomaly detection
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ADFormer: Generalizable Few-Shot Anomaly Detection With Dual CNN-Transformer Architecture
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Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104151 · Cited by 7 · see citing works
AnomalyControl: Few-Shot Anomaly Generation by ControlNet Inpainting
Ali, M., Fioraio, N., Salti, S., & Stefano, L. D. (2024) · IEEE Access · doi:10.1109/access.2024.3520002 · Cited by 7 · see citing works
FocusCLIP: Focusing on Anomaly Regions by Visual-Text Discrepancies
Zhao, Y., Sun, J., Zhang, L., & Lu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3524784 · Cited by 7 · see citing works
- Authors
- Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., & Dabeer, O.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Amazon (United States) (US)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 19606-19616
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386065385
- Semantic Scholar
- aa207668318fec38d60b79f407fb64982e46fce9
- Cited by
- 585
- References
- 58
- Citations by year
- 2026: 286 · 2025: 206 · 2024: 89 · 2023: 4
- FWCI
- 253.73
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- anomaly segmentation, zero-shot learning, few-shot learning, CLIP, vision-language models, prompt templates, industrial inspection, MVTec AD, Visa
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