Jiaqi Liu

I received BS degree from Dalian University of Technology in 2019, and am pursuing the MS degree from Southern University of Science and Technology, Department of Computer Science and Engineering . My research interests include image anomaly detection, image generation and image editing. I am expected to graduate in July 2024.

Email / CV / 中文版简历 / Google Scholar / Github

Education

Southern University of Science and Technology, Department of Computer Science and Engineering, 2021.08-2024.07

Dalian University of Technology, School of Software Technology, 2015.09-2019.06

Working Experience

Tencent, Youtu Lab, 2022.01-Now

Shenzhen Stock Exchange, Shenzhen Securities Communication Co., Ltd. 2019.07-2021.06

Awards

National Scholarship (rank #1 in Department of Computer Science and Engineering, SUSTech), 2023

College of Engineering Academic Star(3‰, SUSTech), 2023

Service

NeurIPS 2023, ICLR 2024, ICML 2024 Reviewer.

Create and maintain repository: https://github.com/M-3LAB/awesome-industrial-anomaly-detection(700+star).

Research

I'm interested in anomaly detection, image generation, computer vision and machine learning.

Note that *contributed equally

	Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt Jiaqi Liu, Kai Wu, Qiang Nie, Ying Chen, Binbin Gao, Yong Liu, jinbao Wang, Chengjie Wang, Feng Zheng AAAI, 2024 paper code We introduce a novel Unsupervised Continual AD framework and augment it with SAM.
	IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing Guoyang Xie,Jinbao Wang, Jiaqi Liu* , Jiayi Lyu, Yong Liu, Chengjie Wang, Feng Zheng, Yaochu Jin IEEE Transactions on Cybernetics(IF: 11.8), 2024 project page / paper We propose a large-scale systematic benchmark and uniform setting for IAD to bridge the gap between academy and industrial manufacturing
	Deep Industrial Image Anomaly Detection: A Survey Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, Yaochu Jin Machine Intelligence Research (CiteScore 8.4)*, 2023 project page / paper We provide a comprehensive review of deep learning-based IAD from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
	Real3D-AD: A Dataset of Point Cloud Anomaly Detection Jiaqi Liu, Guoyang Xie, Ruitao Chen, Xinpeng Li, Jinbao Wang, Yong Liu, Chengjie Wang, Feng Zheng NeurIPS*, 2023 paper code We introduce a 3D point cloud dataset for industrial anomaly detection.
	Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore Guoyang Xie, Jinbao Wang, Jiaqi Liu* , Feng Zheng, Yaochu Jin ICLR, 2023 paper We reveal that rotation-invariant feature property has a significant impact in industrial-based fewshot anomaly detection.
	EasyNet: An Easy Network for 3D Industrial Anomaly Detection Ruitao Chen, Guoyang Xie, Jiaqi Liu* , Jinbao Wang, Ziqi Luo, Jinfan Wang, Feng Zheng ACM MM, 2023 paper We introduce an RGBD anomaly detection algorithm that does not rely on pre-trained models or memory bank.
	What Makes a Good Data Augmentation for Few-Shot Unsupervised Image Anomaly Detection Lingrui Zhang, Shuheng Zhang, Guoyang Xie, Jiaqi Liu, Hua Yan, Jinbao Wang, Feng Zheng, Yaochu Jin CVPRW, 2023 paper We systematically investigate various data augmentation methods for few-shot IAD algorithms.

Feel free to steal this website's source code. Thanks to JonBarron.