# Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection

arXiv: https://arxiv.org/abs/2410.10289 · HTML: https://mlsanity.com/paper/2410.10289/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 73 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 30 of 52)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation](https://mlsanity.com/paper/2303.14814/) (2023)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2104.10036/) (2021)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models](https://mlsanity.com/paper/2308.15366/) (2023)
- [DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection](https://mlsanity.com/paper/2211.11317/) (2022)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)
- [APRIL-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 AD](https://mlsanity.com/paper/2305.17382/) (2023)

## Built on by (most cited first, 22 of 22)

- [Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2502.07601/) (2025)
- [MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval](https://mlsanity.com/paper/2602.00522/) (2026)
- [One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection](https://mlsanity.com/paper/2601.05552/) (2026)
- [AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors](https://mlsanity.com/paper/2601.20524/) (2026)
- [MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples](https://mlsanity.com/paper/2511.10047/) (2025)
- [PromptMoE: Generalizable Zero-Shot Anomaly Detection via Visually-Guided Prompt Mixtures](https://mlsanity.com/paper/2511.18116/) (2025)
- [Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts](https://mlsanity.com/paper/2507.16946/) (2025)
- [CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2508.03447/) (2025)
- [AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models](https://mlsanity.com/paper/2603.01305/) (2026)
- [FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement](https://mlsanity.com/paper/2603.19608/) (2026)
- [AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection](https://mlsanity.com/paper/2605.30140/) (2026)
- [TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2510.21171/) (2025)
- [Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation](https://mlsanity.com/paper/2512.09446/) (2025)
- [SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2601.09147/) (2026)
- [Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning](https://mlsanity.com/paper/2602.03530/) (2026)
- [Unleashing Vision-Language Semantics for Deepfake Video Detection](https://mlsanity.com/paper/2603.24454/) (2026)
- [InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation](https://mlsanity.com/paper/2604.04632/) (2026)
- [H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2604.14507/) (2026)
- [LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection](https://mlsanity.com/paper/2605.15054/) (2026)
- [Beyond Normal References: Discriminative Few-Shot Anomaly Detection](https://mlsanity.com/paper/2605.23231/) (2026)
- [Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors](https://mlsanity.com/paper/2606.29428/) (2026)
- [TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization](https://mlsanity.com/paper/2609.39033/) (2026)

## Abstract (arXiv)

Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these methods often focus on crafting/learning prompts that capture only coarse-grained semantics of abnormality, e.g., high-level semantics like "damaged", "imperfect", or "defective" objects. They therefore have limited capability in recognizing diverse abnormality details that deviate from these general abnormal patterns in various ways. To address this limitation, we propose FAPrompt, a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD. To this end, a novel Compound Abnormality Prompt learning (CAP) module is introduced in FAPrompt to learn a set of complementary, decomposed abnormality prompts, where abnormality prompts are enforced to model diverse abnormal patterns derived from the same normality semantic. On the other hand, the fine-grained abnormality patterns can be different from one dataset to another. To enhance the cross-dataset generalization, another novel module, namely Data-dependent Abnormality Prior learning (DAP), is introduced in FAPrompt to learn a sample-wise abnormality prior from abnormal features of each test image to dynamically adapt the abnormality prompts to individual test images. Comprehensive experiments on 19 real-world datasets, covering both industrial defects and medical anomalies, demonstrate that FAPrompt substantially outperforms state-of-the-art methods in both image- and pixel-level ZSAD tasks. Code is available at https://github.com/mala-lab/FAPrompt.