Learning Generalist Models for Anomaly Detection

Generalist Anomaly Detection (GAD) aims to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data.
Work to be published at CVPR 2024 [1].

Overview

Some recent studies have showed that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets, but their methods rely heavily on handcrafted text prompts about defects, making them difficult to …

anomaly detection foundation-models in-context learning vision language model