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Cited by 585Industrial Vision Systems and Defect DetectionAnomaly Detection Techniques and ApplicationsDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.

WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

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Jeong, Jongheon, Zou, Yang, Kim, Taewan, Zhang, Dongqing, Ravichandran, Avinash, Dabeer, Onkar·2023·2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr52729.2023.01878
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Abstract

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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Research works printed but not linked: 4

  1. geoffrey, 2012, ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems, 25
  2. ilharco, 2021, Open cl
  3. 10.1109/CVPR46437.2021.01466 · DOI · Paper page
  4. alayrac, 2022, Flamingo: A visuallanguage model for few-shot learning, ArXiv Preprint

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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)
ORCID
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)
DOI
10.1109/cvpr52729.2023.01878
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
Licence
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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