{"id":4055,"date":"2022-07-15T16:45:10","date_gmt":"2022-07-15T08:45:10","guid":{"rendered":"https:\/\/inventec2.mjitec.tw\/?page_id=4055"},"modified":"2023-07-18T14:07:25","modified_gmt":"2023-07-18T06:07:25","slug":"domain-generalized-textured-surface","status":"publish","type":"page","link":"https:\/\/inventec2.mjitec.tw\/zh-hans\/ai\/domain-generalized-textured-surface\/","title":{"rendered":"Domain-Generalized Textured Surface"},"content":{"rendered":"

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Domain-Generalized Textured Surface Anomaly Detection <\/h2>\n\t <\/div><\/div>[vc_column_text css=”.vc_custom_1660543559034{margin-bottom: 20px !important;}”]<\/p>\n
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IEEE International Conference on Multimedia and Expo (ICME) 2022<\/p>\n<\/div>\n

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Shang-Fu Chen, Yu-Min Liu, Chia-Ching Lin, Trista Pei-Chun Chen, Yu-Chiang Frank Wang<\/p>\n<\/div>\n

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Jul-22<\/p>\n<\/div>\n

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\u6982\u8981 <\/h2>\n\t <\/div><\/div>[\/vc_column_inner][vc_column_inner el_class=”m_p” width=”2\/3″][vc_column_text]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.<\/p>\n

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.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row][vc_column]

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