@inproceedings{748dcc19a4b84a5e89a8476cd37f58c0,
title = "PGTNET: PROTOTYPE GUIDED TRANSFER NETWORK FOR FEW-SHOT ANOMALY LOCALIZATION",
abstract = "Anomaly localization is pixel-level regions detection in the image. The challenge is how to generate accurate representations of the novel anomaly types which are multifarious. Besides, the anomaly sample size is often not enough to support model learning to detection because of the limitations of real conditions. In this work, we present a novel few-shot setting for anomaly detection and reorganize the defective datasets. Based on the few-shot learning, we transfer the idea of metric learning and propose the prototype-guided transfer network (PGTNet). Extensive experiment results suggest that PGTNet outperforms current SOTA methods and provides a novel perspective for the anomaly localization task.",
keywords = "Anomaly Detection, Few-Shot Learning, Metric Learning",
author = "Zisong Zhuang and Junhang Zhang and Luwei Xiao and Tianlong Ma and Liang He",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th IEEE International Conference on Image Processing, ICIP 2022 ; Conference date: 16-10-2022 Through 19-10-2022",
year = "2022",
doi = "10.1109/ICIP46576.2022.9897566",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2321--2325",
booktitle = "2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings",
address = "美国",
}