VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
Pankaj Kumar Mishra, Riccardo Verk, Daniele Fornasier, Claudio Piciarelli, Gian Luca Foresti
Abstract
We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps preserving the spatial information of the embedded patches, which is later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.
Topics & Concepts
MNIST databaseAnomaly detectionTransformerComputer scienceEmbeddingArtificial intelligenceGaussianPattern recognition (psychology)Computer visionData miningArtificial neural networkEngineeringElectrical engineeringVoltagePhysicsQuantum mechanicsAnomaly Detection Techniques and ApplicationsCOVID-19 diagnosis using AICurrency Recognition and Detection