https://scholars.lib.ntu.edu.tw/handle/123456789/632710
標題: | Domain-Generalized Textured Surface Anomaly Detection | 作者: | Chen, Shang Fu Liu, Yu Min Liu, Chia Ching Chen, Trista Pei Chun YU-CHIANG WANG |
公開日期: | 1-一月-2022 | 卷: | 2022-July | 來源出版物: | Proceedings - IEEE International Conference on Multimedia and Expo | 摘要: | Anomaly detection aims to identify abnormal data that deviates from the normal ones, while typically requiring a sufficient amount of normal data to train the model for performing this task. Despite the success of recent anomaly detection methods, performing anomaly detection in an unseen domain remain a challenging task. In this paper, we address the task of domain-generalized textured surface anomaly detection. By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing. Although with only image-level labels observed in the training data, our patch-based meta-learning model exhibits promising generalization ability: not only can it generalize to unseen image domains, but it can also localize abnormal regions in the query image. Our experiments verify that our model performs favorably against state-of-the-art anomaly detection and domain generalization approaches in various settings. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/632710 | ISBN: | 9781665485630 | ISSN: | 19457871 | DOI: | 10.1109/ICME52920.2022.9859637 |
顯示於: | 電機工程學系 |
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