mlsanityData to 14 Oct 2026

VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation

arXiv 2407.12276 ↗First posted 17 Jul 2024cs.CVcited by 50

On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection

Abstract (arXiv)

Recently, large-scale vision-language models such as CLIP have demonstrated immense potential in zero-shot anomaly segmentation (ZSAS) task, utilizing a unified model to directly detect anomalies on any unseen product with painstakingly crafted text prompts. However, existing methods often assume that the product category to be inspected is known, thus setting product-specific text prompts, which is difficult to achieve in the data privacy scenarios. Moreover, even the same type of product exhibits significant differences due to specific components and variations in the production process, posing significant challenges to the design of text prompts. In this end, we propose a visual context prompting model (VCP-CLIP) for ZSAS task based on CLIP. The insight behind VCP-CLIP is to employ visual context prompting to activate CLIP's anomalous semantic perception ability. In specific, we first design a Pre-VCP module to embed global visual information into the text prompt, thus eliminating the necessity for product-specific prompts. Then, we propose a novel Post-VCP module, that adjusts the text embeddings utilizing the fine-grained features of the images. In extensive experiments conducted on 10 real-world industrial anomaly segmentation datasets, VCP-CLIP achieved state-of-the-art performance in ZSAS task. The code is available at https://github.com/xiaozhen228/VCP-CLIP.

Key facts. Cited by 50 papers in the map (data through 14 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 25 of its 37 references that are in the map. First posted on arXiv 17 Jul 2024.

Builds on

25 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models29 Aug

Other references · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2023Segment Anythingcited by 8,234
  6. 2016Layer Normalizationcited by 5,669
  7. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  8. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  9. 2022Visual Prompt Tuningcited by 1,681
  10. 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,433
  11. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 532
  12. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
12 more
  1. 2023Faster Segment Anything: Towards Lightweight SAM for Mobile Applicationscited by 352
  2. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  3. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  4. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  5. 2022ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentationcited by 178
  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. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  8. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  9. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 60
  10. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  11. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  12. 2021Industrial Machine Tool Component Surface Defect Datasetcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 27; 2026: 19.

50 papers.

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  1. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 73
  2. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  3. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31
  4. 2025AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detectioncited by 29
  5. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  6. 2025Prompt-based Adaptation in Large-scale Vision Models: A Surveycited by 21
  7. 2026VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformercited by 11
  8. 2025DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookupcited by 10
  9. 2026MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrievalcited by 6
  10. 2024RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observationscited by 5
  11. 2025A Survey on Foundation-Model-Based Industrial Defect Detectioncited by 5
  12. 2025UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptationcited by 5
38 more
  1. 2025Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detectioncited by 4
  2. 2026One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detectioncited by 4
  3. 2026GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learningcited by 4
  4. 2024ONER: Online Experience Replay for Incremental Anomaly Detectioncited by 3
  5. 2025Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detectioncited by 3
  6. 2025GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detectioncited by 3
  7. 2025MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samplescited by 3
  8. 2025PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awarenesscited by 2
  9. 2025Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillationcited by 2
  10. 2025Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detectioncited by 2
  11. 2025Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Eracited by 2
  12. 2025PromptMoE: Generalizable Zero-Shot Anomaly Detection via Visually-Guided Prompt Mixturescited by 2
  13. 2026VitaTouch: Property-Aware Vision-Tactile-Language Model for Robotic Quality Inspection in Manufacturingcited by 2
  14. 2025IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domaincited by 1
  15. 2025Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Conceptscited by 1
  16. 2025CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detectioncited by 1
  17. 2025A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detectioncited by 1
  18. 2025PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detectioncited by 1
  19. 2025PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detectioncited by 1
  20. 2026AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Modelscited by 1
  21. 2026SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detectioncited by 1
  22. 2026Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentationcited by 1
  23. 2025ViP2-CLIP: Visual-Perception Prompting with Unified Alignment for Zero-Shot Anomaly Detectioncited by 0
  24. 2025CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detectioncited by 0
  25. 2025Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detectioncited by 0
  26. 2025Language-in-the-Loop Culvert Inspection on the Erie Canalcited by 0
  27. 2025AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detectioncited by 0
  28. 2026MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detectioncited by 0
  29. 2026Hyper-FSAD: Training-Free and Language-Free Few-Shot Anomaly Detection via Sparse Hyper Matchingcited by 0
  30. 2026Res2CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignmentcited by 0
  31. 2026DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Modelscited by 0
  32. 2026CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detectioncited by 0
  33. 2026VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detectioncited by 0
  34. 2026PADFormer: Pose-agnostic Anomaly Detection from Sparse View Imagescited by 0
  35. 2026Dual Anchors, Do It Better: Hierarchical Group Merging for Zero-Shot Anomaly Detectioncited by 0
  36. 2026Multi-Modal Anomaly Detection: A Surveycited by 0
  37. 2026Co-Evolutionary Prompt Optimization with Cross-Category Transfer for Zero-Shot Anomaly Detectioncited by 0
  38. 2026Anchor and Adapt: Asymmetric Prompt Adaptation for Few-Shot Industrial Anomaly Detectioncited by 0

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  1. 2026Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection
  2. 2025KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
  3. 2026SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection
  4. 2024Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection
  5. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
  6. 2026NOVA: Normal-Side Modeling for Training-Free Zero-Shot Video Anomaly Detection
  7. 2025Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
  8. 2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection