title: Learning to Detect Multi-class Anomalies with Just One Normal Image
    Prompt
summary: Unsupervised reconstruction networks using self-attention transformers have
  achieved state-of-the-art performance for multi-class (unified) anomaly
  detection with a single model. However, these self-attention reconstruction
  models primarily operate on target features, which may result in perfect
  reconstruction for both normal and anomaly features due to high consistency
  with context, leading to failure in detecting anomalies. Additionally, these
  models often produce inaccurate anomaly segmentation due to performing
  reconstruction in a low spatial resolution latent space. To enable
  reconstruction models enjoying high efficiency while enhancing their
  generalization for unified anomaly detection, we propose a simple yet effective
  method that reconstructs normal features and restores anomaly features with
  just One Normal Image Prompt (OneNIP). In contrast to previous work, OneNIP
  allows for the first time to reconstruct or restore anomalies with just one
  normal image prompt, effectively boosting unified anomaly detection
  performance. Furthermore, we propose a supervised refiner that regresses
  reconstruction errors by using both real normal and synthesized anomalous
  images, which significantly improves pixel-level anomaly segmentation. OneNIP
  outperforms previous methods on three industry anomaly detection benchmarks:
  MVTec, BTAD, and VisA. The code and pre-trained models are available at
  https://github.com/gaobb/OneNIP.
paper_authors._id: 682548bff4997d78fe92cd57
paper_authors.name: Bin-Bin Gao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-15T01:54:51.290Z
paper_authors._id: 648972ff99f6c45ff6bbd295
paper_authors.avatarUrl: /avatars/d1fe9d631a786462d55cfe43c565c88e.svg
paper_authors.fullname: Bin-Bin Gao
paper_authors.type: user
paper_authors.user: csgaobb
paper_title: Learning to Detect Multi-class Anomalies with Just One Normal Image
    Prompt
paper_summary: Unsupervised reconstruction networks using self-attention transformers have
  achieved state-of-the-art performance for multi-class (unified) anomaly
  detection with a single model. However, these self-attention reconstruction
  models primarily operate on target features, which may result in perfect
  reconstruction for both normal and anomaly features due to high consistency
  with context, leading to failure in detecting anomalies. Additionally, these
  models often produce inaccurate anomaly segmentation due to performing
  reconstruction in a low spatial resolution latent space. To enable
  reconstruction models enjoying high efficiency while enhancing their
  generalization for unified anomaly detection, we propose a simple yet effective
  method that reconstructs normal features and restores anomaly features with
  just One Normal Image Prompt (OneNIP). In contrast to previous work, OneNIP
  allows for the first time to reconstruct or restore anomalies with just one
  normal image prompt, effectively boosting unified anomaly detection
  performance. Furthermore, we propose a supervised refiner that regresses
  reconstruction errors by using both real normal and synthesized anomalous
  images, which significantly improves pixel-level anomaly segmentation. OneNIP
  outperforms previous methods on three industry anomaly detection benchmarks:
  MVTec, BTAD, and VisA. The code and pre-trained models are available at
  https://github.com/gaobb/OneNIP.
paper_ai_summary: OneNIP method enhances unified anomaly detection by reconstructing anomalies using a single normal image prompt and a supervised refiner for improved segmentation.
paper_ai_keywords: self-attention transformers anomaly detection feature reconstruction anomaly segmentation latent space One Normal Image Prompt (OneNIP) supervised refiner MVTec BTAD VisA