no code implementations • 7 Apr 2024 • Aofan Jiang, Chaoqin Huang, Qing Cao, Yuchen Xu, Zi Zeng, Kang Chen, Ya zhang, Yanfeng Wang
We introduce a novel self-supervised learning framework for ECG AD, utilizing a vast dataset of normal ECGs to autonomously detect and localize cardiac anomalies.
Self-Supervised Anomaly Detection Self-Supervised Learning +2
1 code implementation • 19 Mar 2024 • Chaoqin Huang, Aofan Jiang, Jinghao Feng, Ya zhang, Xinchao Wang, Yanfeng Wang
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains.
no code implementations • 9 Aug 2023 • Chaoqin Huang, Aofan Jiang, Ya zhang, Yanfeng Wang
Anomaly detection has gained considerable attention due to its broad range of applications, particularly in industrial defect detection.
1 code implementation • 3 Aug 2023 • Aofan Jiang, Chaoqin Huang, Qing Cao, Shuang Wu, Zi Zeng, Kang Chen, Ya zhang, Yanfeng Wang
To address this challenge, this paper introduces a novel multi-scale cross-restoration framework for ECG anomaly detection and localization that considers both local and global ECG characteristics.
1 code implementation • 15 Jul 2022 • Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya zhang, Michael Spratling, Yan-Feng Wang
Inspired by how humans detect anomalies, i. e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model.
Ranked #72 on Anomaly Detection on MVTec AD