# Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

arXiv: https://arxiv.org/abs/2503.02424 · HTML: https://mlsanity.com/paper/2503.02424/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 53 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 30 of 40)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [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)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [A Unified Model for Multi-class Anomaly Detection](https://mlsanity.com/paper/2206.03687/) (2022)
- [SimpleNet: A Simple Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2303.15140/) (2023)
- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [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)
- [Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection](https://mlsanity.com/paper/2403.12580/) (2024)
- [Registration based Few-Shot Anomaly Detection](https://mlsanity.com/paper/2207.07361/) (2022)
- [AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2407.15795/) (2024)
- [RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection](https://mlsanity.com/paper/2403.05897/) (2024)
- [MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2404.06564/) (2024)
- [PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2404.05231/) (2024)
- [Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2405.14325/) (2024)
- [Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts](https://mlsanity.com/paper/2403.06495/) (2024)
- [Stable and low-precision training for large-scale vision-language models](https://mlsanity.com/paper/2304.13013/) (2023)
- [Learning Normal Dynamics in Videos with Meta Prototype Network](https://mlsanity.com/paper/2104.06689/) (2021)
- [A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect](https://mlsanity.com/paper/2401.16402/) (2024)
- [Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images](https://mlsanity.com/paper/2403.12570/) (2024)

## Built on by (most cited first, 30 of 48)

- [Normal-Abnormal Guided Generalist Anomaly Detection](https://mlsanity.com/paper/2510.00495/) (2025)
- [Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection](https://mlsanity.com/paper/2505.17551/) (2025)
- [INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning](https://mlsanity.com/paper/2506.03660/) (2025)
- [Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2510.17611/) (2025)
- [A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features](https://mlsanity.com/paper/2511.13115/) (2025)
- [Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark](https://mlsanity.com/paper/2505.10996/) (2025)
- [AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors](https://mlsanity.com/paper/2601.20524/) (2026)
- [RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images](https://mlsanity.com/paper/2505.21152/) (2025)
- [Dual Distillation for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2603.01713/) (2026)
- [From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge](https://mlsanity.com/paper/2509.17615/) (2025)
- [ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning](https://mlsanity.com/paper/2509.23741/) (2025)
- [Search is All You Need for Few-shot Anomaly Detection](https://mlsanity.com/paper/2504.11895/) (2025)
- [Leveraging Learning Bias for Noisy Anomaly Detection](https://mlsanity.com/paper/2508.07441/) (2025)
- [AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI](https://mlsanity.com/paper/2512.21264/) (2025)
- [MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2604.10971/) (2026)
- [SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track](https://mlsanity.com/paper/2605.14808/) (2026)
- [Memory-Distilled Selection for Noise-Robust Anomaly Detection](https://mlsanity.com/paper/2605.26676/) (2026)
- [Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization](https://mlsanity.com/paper/2605.28428/) (2026)
- [ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection](https://mlsanity.com/paper/2607.04894/) (2026)
- [IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion](https://mlsanity.com/paper/2607.19032/) (2026)
- [A VLM Answer Is Not an Anomaly Score: Rank Compression Across Image and Video Anomaly Detection](https://mlsanity.com/paper/2608.21244/) (2026)
- [Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2506.13097/) (2025)
- [Multi-View Reconstruction with Global Context for 3D Anomaly Detection](https://mlsanity.com/paper/2507.21555/) (2025)
- [Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection](https://mlsanity.com/paper/2508.01591/) (2025)
- [Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection](https://mlsanity.com/paper/2508.19573/) (2025)
- [Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization](https://mlsanity.com/paper/2509.05034/) (2025)
- [Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset](https://mlsanity.com/paper/2509.18919/) (2025)
- [ProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarios](https://mlsanity.com/paper/2511.12938/) (2025)
- [HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection](https://mlsanity.com/paper/2602.05670/) (2026)
- [A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection](https://mlsanity.com/paper/2603.22861/) (2026)

## Abstract (arXiv)

Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Code is available at:https://github.com/luow23/INP-Former.