mlsanityData to 14 Oct 2026

Zero-Shot Anomaly Detection with Pre-trained Segmentation Models

arXiv 2306.09269 ↗First posted 15 Jun 2023cs.CV cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the performance of the WINCLIP framework, we aim to enhance the system's localization capabilities by integrating zero-shot segmentation models. In addition, we perform foreground instance segmentation which enables the model to focus on the relevant parts of the image, thus allowing the models to better identify small or subtle deviations. Our pipeline requires no external data or information, allowing for it to be directly applied to new datasets. Our team (Variance Vigilance Vanguard) ranked third in the zero-shot track of the VAND challenge, and achieve an average F1-max score of 81.5/24.2 at a sample/pixel level on the VisA dataset.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

7 of 10 references.

References · 7

  1. 2023Segment Anythingcited by 8,234
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  3. 2021Image Segmentation Using Text and Image Promptscited by 512
  4. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  5. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  6. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 130
  7. 2022Highly Accurate Dichotomous Image Segmentationcited by 110

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.

8 papers.

  1. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  2. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  3. 2025EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Modelscited by 11
  4. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  5. 2024SOS: Segment Object System for Open-World Instance Segmentation With Object Priorscited by 3
  6. 2025StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detectioncited by 2
  7. 2025A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detectioncited by 1
  8. 2026Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervisioncited by 0