Anomaly Image Generation under the Guidance of Knowledge Graph
Abstract
Abstract The goal of our approach is to guide the text prompt construction of anomalous images by leveraging prior knowledge about the anomalies, their characteristics, and co-existence. This information is represented in a Knowledge Graph (KG) comprising classes of various products and anomalies, along with their relationships and the augmentations that can be applied. The ultimate task is to train and evaluate foundation models based on a spatial semantic representation of anomalies and a suitable diffusion model capable of learning forms of anomalies, and generating them for objects of the same class or augmenting them.
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