@inproceedings{225fa03b113e47d1a731fee555161851,
title = "Advancing Robust Few-shot Surface Defect Detection through Meta-learning",
abstract = "The primary limitation of deep learning (DL) techniques in detecting manufacturing defects is the requirement for a substantial volume of labeled data. Nevertheless, obtaining precise annotations is an arduous process due to the diverse range of defect categories. To tackle this issue, we propose MetaFormer, a transformer-based few-shot surface defect detection that learns the meta-features from a large-scale dataset of base classes. This method enables generalization to a novel class with only a few training examples. We demonstrate the effectiveness of our model on the steel surface defect dataset, surpassing the performance of the baseline model despite using only a limited number of annotated examples.",
keywords = "few-shot detection, object detection, surface defect detection",
author = "Putri, {Wenny Ramadha} and Yung-Hui Li and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024 ; Conference date: 06-06-2024 Through 08-06-2024",
year = "2024",
doi = "10.1109/ICDV61346.2024.10616593",
language = "???core.languages.en_GB???",
series = "Proceedings of the 2024 9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "45--48",
editor = "Xuan-Tu Tran and Duy-Hieu Bui",
booktitle = "Proceedings of the 2024 9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024",
}