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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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Zhuo, J., Li, R., Gao, M., Yang, G., Yuan, S., & Le, X. (2025) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci65340.2025.11096341
MEN-AD: Multimodal Zero-Shot Detection of Mucus Anomalies in Endoscopy
Yuan, X., Huang, M., Hua, L., Zhang, C., Ju, J., Xie, S., & Gan, W. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-0017-8_8
AgriGPT: A Strong Plant Disease Detection Model via Visual-Language Model
Zheng, J., & Wang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9921-6_20
CLIP-DSA: A CLIP-Based Discriminative and Self-supervised Framework for Few-Shot Anomaly Detection
Zeng, S., Chen, Y., Li, M. N., Wu, Y., & Tian, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9891-2_3
Learning Visual Augmentations from Linguistic Knowledge Prior for Anomaly Detection
Jiang, X., Liu, X., Ding, Z., Sun, X., & Hu, Z. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11091270
Pixel-level Anomaly Detection System for Casting Process Using an Unsupervised Deep Learning Model
Lim, H., Suh, J., & Hur, Y. (2025) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2025.51.4.329
RIASA: Enhancing Reasoning Industrial Anomaly Segmentation via Large Vision-Language Models
Zhang, Z., Gao, X., Ruan, J., Liu, T., & Fu, Y. (2025) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew68306.2025.11152172
AAAD: Adaptive Activated Anomaly Detection on Varied Backgrounds
Miyamoto, K., Lee, T., & Minezawa, A. (2025) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss65446.2025.11149930
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
Park, Y., Kang, S., Kim, M. J., Kim, H. S., & Yi, J. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00377
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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