Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2025
Abstract
Few-shot Anomaly Detection (AD) for images aims to detect anomalies with few-shot normal samples from the target dataset. It is a crucial task when only few samples can be obtained, and it is challenging since it needs to be generalized to different domains. Existing methods try to enhance the generalizability of AD by incorporating large vision-language models (LVLMs).However, how to transform category semantic information in LVLMs into anomaly information to improve the generalizability of AD remains a challenge facing existing methods.To address the challenge, we propose a few-shot AD method called MetaCAN, a novel category-to-anomaly network trained with AD meta-learning scheme based on an LVLM. Specifically, MetaCAN constructs the auxiliary training data and multiple tasks based on different categories to perform AD meta-learning, which ensures that the optimization toward the achievement of optimal anomaly detection across all categories. Moreover, MetaCAN introduces an image-image anomaly discriminator and an image-text anomaly detector to fully exploit the powerful multimodal semantic representations during auxiliary training. Once trained on auxiliary datasets, MetaCAN can be applied directly to other target datasets without retraining. Extensive experiments on six real-world datasets demonstrate that MetaCAN achieves state-of-the-art performance on cross-domain and cross-category anomaly detection tasks compared with existing methods.
Keywords
Anomaly Detection, Meta Learning, Few-shot Learning
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
CIKM '25: Proceedings of the 34th ACM International Conference on Information and Knowledge Management, Seoul, Korea, November 10-14
First Page
2032
Last Page
2041
Identifier
10.1145/3746252.3761253
Publisher
ACM
City or Country
New York
Citation
LV, Zhisheng; ZHANG, Jianfeng; JIAN, Songlei; HUANG, Chenlin; ZHANG, Hongguang; PANG, Guansong; and LIU, Zhong.
MetaCAN: Improving generalizability of few‑shot anomaly detection with meta‑learning. (2025). CIKM '25: Proceedings of the 34th ACM International Conference on Information and Knowledge Management, Seoul, Korea, November 10-14. 2032-2041.
Available at: https://ink.library.smu.edu.sg/sis_research/10840
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3746252.3761253