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
no code implementations • 13 May 2022 • Chaoqin Huang, Qinwei Xu, Yanfeng Wang, Yu Wang, Ya zhang
To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization.
no code implementations • 7 Sep 2021 • Xiaoman Zhang, Weidi Xie, Chaoqin Huang, Yanfeng Wang, Ya zhang, Xin Chen, Qi Tian
In this paper, we target self-supervised representation learning for zero-shot tumor segmentation.
no code implementations • 9 Dec 2020 • Chaoqin Huang, Fei Ye, Peisen Zhao, Ya zhang, Yan-Feng Wang, Qi Tian
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided.
Ranked #25 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
no code implementations • 9 Dec 2020 • Fei Ye, Huangjie Zheng, Chaoqin Huang, Ya zhang
Based on this object function we introduce a novel information theoretic framework for unsupervised image anomaly detection.
Ranked #8 on
Anomaly Detection
on One-class CIFAR-100
1 code implementation • 25 Nov 2019 • Chaoqin Huang, Fei Ye, Jinkun Cao, Maosen Li, Ya zhang, Cewu Lu
We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors.
Ranked #21 on
Anomaly Detection
on One-class CIFAR-10
no code implementations • CVPR 2018 • Bowen Pan, Wuwei Lin, Xiaolin Fang, Chaoqin Huang, Bolei Zhou, Cewu Lu
Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection.