# A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

arXiv: https://arxiv.org/abs/2401.16402 · HTML: https://mlsanity.com/paper/2401.16402/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 67 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 30 of 54)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [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)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation](https://mlsanity.com/paper/2303.14814/) (2023)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [Sequential Modeling Enables Scalable Learning for Large Vision Models](https://mlsanity.com/paper/2312.00785/) (2023)
- [The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization](https://mlsanity.com/paper/2112.09045/) (2021)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Registration based Few-Shot Anomaly Detection](https://mlsanity.com/paper/2207.07361/) (2022)
- [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)
- [Asymmetric Student-Teacher Networks for Industrial Anomaly Detection](https://mlsanity.com/paper/2210.07829/) (2022)
- [AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model](https://mlsanity.com/paper/2312.05767/) (2023)
- [Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection](https://mlsanity.com/paper/2203.14506/) (2022)
- [Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2203.05550/) (2022)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Real3D-AD: A Dataset of Point Cloud Anomaly Detection](https://mlsanity.com/paper/2309.13226/) (2023)
- [Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore](https://mlsanity.com/paper/2301.12082/) (2023)

## Built on by (most cited first, 30 of 69)

- [Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection](https://mlsanity.com/paper/2403.12580/) (2024)
- [AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2407.15795/) (2024)
- [MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2404.06564/) (2024)
- [Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts](https://mlsanity.com/paper/2403.06495/) (2024)
- [Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection](https://mlsanity.com/paper/2503.02424/) (2025)
- [Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey](https://mlsanity.com/paper/2407.21794/) (2024)
- [PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection](https://mlsanity.com/paper/2410.00320/) (2024)
- [Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties](https://mlsanity.com/paper/2412.14592/) (2024)
- [ResAD: A Simple Framework for Class Generalizable Anomaly Detection](https://mlsanity.com/paper/2410.20047/) (2024)
- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
- [M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising](https://mlsanity.com/paper/2406.02263/) (2024)
- [Unseen Visual Anomaly Generation](https://mlsanity.com/paper/2406.01078/) (2024)
- [Generative Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2402.11887/) (2024)
- [Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2410.10289/) (2024)
- [Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning](https://mlsanity.com/paper/2403.11083/) (2024)
- [LogiCode: an LLM-Driven Framework for Logical Anomaly Detection](https://mlsanity.com/paper/2406.04687/) (2024)
- [Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?](https://mlsanity.com/paper/2501.15795/) (2025)
- [Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark](https://mlsanity.com/paper/2404.10760/) (2024)
- [Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework](https://mlsanity.com/paper/2409.13162/) (2024)
- [Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection](https://mlsanity.com/paper/2504.14221/) (2025)
- [Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection](https://mlsanity.com/paper/2503.03562/) (2025)
- [Learning Multi-view Anomaly Detection with Efficient Adaptive Selection](https://mlsanity.com/paper/2407.11935/) (2024)
- [Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation](https://mlsanity.com/paper/2505.24431/) (2025)
- [RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection](https://mlsanity.com/paper/2406.07176/) (2024)
- [Dual-path Frequency Discriminators for Few-shot Anomaly Detection](https://mlsanity.com/paper/2403.04151/) (2024)
- [GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection](https://mlsanity.com/paper/2403.06154/) (2024)
- [A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection](https://mlsanity.com/paper/2406.03262/) (2024)
- [A Survey on Industrial Anomaly Synthesis](https://mlsanity.com/paper/2502.16412/) (2025)
- [INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning](https://mlsanity.com/paper/2506.03660/) (2025)
- [MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation](https://mlsanity.com/paper/2602.07011/) (2026)

## Abstract (arXiv)

Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspection, and medical lesion detection. This survey comprehensively examines recent advancements in VAD by identifying three primary challenges: 1) scarcity of training data, 2) diversity of visual modalities, and 3) complexity of hierarchical anomalies. Starting with a brief overview of the VAD background and its generic concept definitions, we progressively categorize, emphasize, and discuss the latest VAD progress from the perspective of sample number, data modality, and anomaly hierarchy. Through an in-depth analysis of the VAD field, we finally summarize future developments for VAD and conclude the key findings and contributions of this survey.