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Few-shot Online Anomaly Detection and Segmentation
March 28, 2024, 4:45 a.m. | Shenxing Wei, Xing Wei, Zhiheng Ma, Songlin Dong, Shaochen Zhang, Yihong Gong
cs.CV updates on arXiv.org arxiv.org
Abstract: Detecting anomaly patterns from images is a crucial artificial intelligence technique in industrial applications. Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance. Consequently, this paper focuses on addressing the challenging yet practical few-shot online anomaly detection and segmentation (FOADS) task. Under the …
abstract anomaly anomaly detection applications artificial artificial intelligence arxiv cs.cv data deployment detection domain few-shot images industrial intelligence normal patterns practical research samples segmentation training training data type
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