mlsanityData to 14 Oct 2026

PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection

arXiv 2509.25856 ↗First posted 30 Sep 2025cs.CVcited by 1

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Abstract (arXiv)

Industrial anomaly detection is increasingly relying on foundation models, aiming for strong out-of-distribution generalization and rapid adaptation in real-world deployments. Notably, past studies have primarily focused on textual prompt tuning, leaving the intrinsic visual counterpart fragmented into processing steps specific to each foundation model. We aim to address this limitation by proposing a unified patch-focused framework, Patch-Exclusive Anomaly Detection (PatchEAD), enabling training-free anomaly detection that is compatible with diverse foundation models. The framework constructs visual prompting techniques, including an alignment module and foreground masking. Our experiments show superior few-shot and batch zero-shot performance compared to prior work, despite the absence of textual features. Our study further examines how backbone structure and pretrained characteristics affect patch-similarity robustness, providing actionable guidance for selecting and configuring foundation models for real-world visual inspection. These results confirm that a well-unified patch-only framework can enable quick, calibration-light deployment without the need for carefully engineered textual prompts.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 38 of its 51 references that are in the map. First posted on arXiv 30 Sep 2025.

Builds on

38 of 51 references.

References · 38

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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26 more
  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  2. 2022BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizerscited by 303
  3. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  4. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  5. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  6. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
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  12. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
  13. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  14. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  15. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 89
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  19. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
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  21. 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationcited by 50
  22. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  23. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
  24. 2024Few-shot Adaptation of Multi-modal Foundation Models: A Surveycited by 28
  25. 2020TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regionscited by 21
  26. 2023Zero-Shot Anomaly Detection via Batch Normalizationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detectioncited by 0