TY - GEN
T1 - FastRecon
T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
AU - Fang, Zheng
AU - Wang, Xiaoyang
AU - Li, Haocheng
AU - Liu, Jiejie
AU - Hu, Qiugui
AU - Xiao, Jimin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2024/1/15
Y1 - 2024/1/15
N2 - In industrial anomaly detection, data efficiency and the ability for fast migration across products become the main concerns when developing detection algorithms. Existing methods tend to be data-hungry and work in the one-model-one-category way, which hinders their effectiveness in real-world industrial scenarios. In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost. Given a defective query sample, we propose to utilize a few normal samples as a reference to reconstruct its normal version, where the final anomaly detection can be achieved by sample alignment. Specifically, we introduce a novel regression with distribution regularization to obtain the optimal transformation from support to query features, which guarantees the reconstruction result shares visual similarity with the query sample and meanwhile maintains the property of normal samples. Experimental results show that our method significantly outperforms previous state-of-the-art at both image and pixel-level AUROC performances from 2 to 8-shot scenarios. Besides, with only a limited number of training samples (less than 8 samples), our method reaches competitive performance with vanilla AD methods which are trained with extensive normal samples. The code is available at https://github.com/FzJun26th/FastRecon.
AB - In industrial anomaly detection, data efficiency and the ability for fast migration across products become the main concerns when developing detection algorithms. Existing methods tend to be data-hungry and work in the one-model-one-category way, which hinders their effectiveness in real-world industrial scenarios. In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost. Given a defective query sample, we propose to utilize a few normal samples as a reference to reconstruct its normal version, where the final anomaly detection can be achieved by sample alignment. Specifically, we introduce a novel regression with distribution regularization to obtain the optimal transformation from support to query features, which guarantees the reconstruction result shares visual similarity with the query sample and meanwhile maintains the property of normal samples. Experimental results show that our method significantly outperforms previous state-of-the-art at both image and pixel-level AUROC performances from 2 to 8-shot scenarios. Besides, with only a limited number of training samples (less than 8 samples), our method reaches competitive performance with vanilla AD methods which are trained with extensive normal samples. The code is available at https://github.com/FzJun26th/FastRecon.
UR - http://www.scopus.com/inward/record.url?scp=85184908065&partnerID=8YFLogxK
U2 - 10.1109/ICCV51070.2023.01603
DO - 10.1109/ICCV51070.2023.01603
M3 - Conference Proceeding
AN - SCOPUS:85184908065
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 17435
EP - 17444
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 October 2023 through 6 October 2023
ER -