mlsanityData to 7 Oct 2026

SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection

arXiv 2601.09147 ↗First posted 14 Jan 2026cs.CV cs.AIcited by 0

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

Abstract (arXiv)

Zero-Shot Anomaly Detection (ZSAD) leverages Vision-Language Models (VLMs) to enable supervision-free industrial inspection. However, existing ZSAD paradigms are constrained by single visual backbones, which struggle to balance global semantic generalization with fine-grained structural discriminability. To bridge this gap, we propose Synergistic Semantic-Visual Prompting (SSVP), that efficiently fuses diverse visual encodings to elevate model's fine-grained perception. Specifically, SSVP introduces the Hierarchical Semantic-Visual Synergy (HSVS) mechanism, which deeply integrates DINOv3's multi-scale structural priors into the CLIP semantic space. Subsequently, the Vision-Conditioned Prompt Generator (VCPG) employs cross-modal attention to guide dynamic prompt generation, enabling linguistic queries to precisely anchor to specific anomaly patterns. Furthermore, to address the discrepancy between global scoring and local evidence, the Visual-Text Anomaly Mapper (VTAM) establishes a dual-gated calibration paradigm. Extensive evaluations on seven industrial benchmarks validate the robustness of our method; SSVP achieves state-of-the-art performance with 93.0% Image-AUROC and 92.2% Pixel-AUROC on MVTec-AD, significantly outperforming existing zero-shot approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 33 of its 55 references that are in the map. First posted on arXiv 14 Jan 2026.

Builds on

33 of 55 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection13 Mar
  2. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models11 Feb

Other references · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  4. 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,427
  5. 2025DINOv3cited by 1,297
  6. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  7. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 419
  8. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  9. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 311
  10. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  11. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  12. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
19 more
  1. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  2. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  3. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  4. 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 128
  5. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  6. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  7. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  8. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  9. 2024MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Imagescited by 52
  10. 2022Bayesian Prompt Learning for Image-Language Model Generalizationcited by 51
  11. 2024FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localizationcited by 51
  12. 2025AI Flow: Perspectives, Scenarios, and Approachescited by 44
  13. 2024SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Foldingcited by 42
  14. 2025LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extractioncited by 24
  15. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  16. 2021NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognitioncited by 12
  17. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  18. 2025Cluster-Aware Prompt Ensemble Learning for Few-Shot Vision-Language Model Adaptationcited by 7
  19. 2018Transfer Metric Learning: Algorithms, Applications and Outlookscited by 2

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  1. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
  2. 2025KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
  3. 2026A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision
  4. 2024VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
  5. 2024Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
  6. 2026VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
  7. 2025Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
  8. 2026TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection