Learning to Detect Multi-class Anomalies with Just One Normal Image
  Prompt
Authors: name: Bin-Bin Gao

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.