Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

arXiv:2403.06495v1 Announce Type: new
Abstract: This paper explores the problem of Generalist Anomaly Detection (GAD), aiming 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. Some recent studies have shown 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 …

abstract anomaly anomaly detection application arxiv context cs.cv data datasets detection diverse domains few-shot paper prompts residual sample studies train training type via