周毅

发布者:周毅发布时间:2021-03-08浏览次数:739


Yi Zhou (周毅 Joey)

    Ph.D., Associate Professor, IEEE Member, CCF Member, Google Scholar

    School of Computer Science and Engineering, Southeast University

    I am a member of PAttern Learning and Mining(PALM) Lab. 

    Office: Room 150, School of Computer Science and Engineering, Southeast University Jiulonghu Campus, Nanjing, Jiangsu, China.



Brief Biography

Dr. Yi Zhou is currently an associate professor with the school of computer science and engineering, Southeast University, China. Before joining SEU, he was a research scientist for three years, with the Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates. He received the Ph.D. degree from the school of computing sciences, University of East Anglia, U.K., in 2018 and the M.Sc. degree from the department of electronic and electrical engineering, University of Sheffield, U.K., in 2014. His research interests include computer vision, pattern recognition, machine learning, and medical imaging. He has authored/co-authored 25+ academic papers in top journals/conferences such as CVPR, ECCV, AAAI, MICCAI, ICRA, IEEE Trans. on Image Processing, and IEEE Trans. on Medical Imaging. He has also owned two US patents, as the first inventor.

Ph.D. Supervisor: Professor Ling Shao (CEO of IIAI, Ph.D. from University of Oxford)


周毅,1990年出生于江苏苏州,现任东南大学计算机科学与工程学院副教授,在PALM实验室工作。2013年至2018年,获全额奖学金,分别赴英国谢菲尔德大学英国东安格利亚大学留学,师从邵岭教授,并获得硕士、博士学位。2018年至2021年加入阿联酋起源人工智能研究院(IIAI),担任研究科学家。研究工作领域主要包括:计算机视觉,模式识别,机器学习,深度学习,医学影像分析与识别,智能图像视频理解等。周毅已在领域内国际公认的顶级期刊会议(例如CVPR, AAAI, ECCV, TIP, TMI等)发表25+篇论文,并拥有2项美国发明专利。学术兼职包括IEEE会员、CCF会员,并担任多项国际顶级期刊会议审稿人。

欢迎对我研究方向有浓厚兴趣,数学优秀,编程能力扎实的同学,与我一起工作!


Research Interests

Computer Vision - Object Detection, Image/Video Object Segmentation, Person/Vehicle Re-identification

Machine Learning, Deep Learning - Unsupervised Domain Adaptation, Self-supervised Learning, Learning from Noisy Data, Generative Adversarial Networks, Knowledge Distillation.

Medical Imaging - Fundus Imaging, X-ray, CT, MRI



Selected Publications

Huang, L., Zhou, Y., Liu, L., Zhu, F., and Shao L., 2021. Group Whitening: Balancing Learning Efficiency and Representational Capacity. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Online. IEEE.

Zhou, Y., Zhou, T., Zhou, T., Fu, H., and Shao, L., 2021. Contrastive-Attentive Thoracic Disease Recognition with Dual-Weighting Graph Reasoning. IEEE Transactions on Medical Imaging. DOI: 10.1109/TMI.2021.3049498

Zhou, Y., Wang, B., He, X., Cui, S., and Shao, L., 2021. DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images. IEEE Journal of Biomedical and Health Informatics. DOI: 10.1109/JBHI.2020.3045475

Zhou, Y., Huang, L., Zhou, T., and Shao L., 2021. Many-to-One Distribution Learning and K-Nearest Neighbour Smoothing for Thoracic Disease Identification. Thirty-Fifth AAAI Conference on Artificial Intelligence, Virtual Conference.

Li, X., Zhou, T., Li J., Zhou, Y., and Zhang, Z., 2021. Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation. Thirty-Fifth AAAI Conference on Artificial Intelligence, Virtual Conference.

Zhou, Y., Wang, B., Huang, L., Cui, S., and Shao, L., 2020. A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability. IEEE Transactions on Medical Imaging. DOI: 10.1109/TMI.2020.3037771

Fan, D., Zhou, T., Ji, G., Zhou, Y., Chen, G., Fu, H., Shen, J., and Shao, L., 2020. Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging. DOI: 10.1109/TMI.2020.2996645

Huang, L., Zhao, L., Zhou, Y., Zhu, F., Liu, L. and Shao, L., 2020, June. An Investigation into the Stohastic of Batch Whitening. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. IEEE. [Oral]

Zhou, T., Wang, S., Zhou, Y., Yao, Y., Li, J. and Shao, L., 2020, Feb. Motion-Attentive Transition for Zero-Shot Video Object Segmentation. Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, USA.

Zhou, Y., He, X., Cui., S, Zhu, F., Liu., L and Shao, L., 2019, Oct. High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions. International Conference on Medical Imaging Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China.

He, X., Zhou, Y., Wang, B., Cui, S. and Shao, L., 2019, Oct. DME-Net: Diabetic Macular Edema Grading by Auxiliary Task Learning. International Conference on Medical Imaging Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China.

Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S. and Shao, L., 2019, Jun. Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA. IEEE.

Huang, L., Zhou, Y., Zhu, F., Liu, L., and Shao, L., 2019, Jun. Iterative Normalization: Beyond Standardization towards Efficient Whitening. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA. IEEE.

Zhen, W., Zhang, J., Liu, L., Zhu, F., Shen, Zhou, Y., F., Sun, Y., Liu, S., Shao, L., 2019, Jun. Building Detail-Sensitive Semantic Segmentation Networks with Polynomial Pooling. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA. IEEE.

Zhou, Y. and Shao, L., 2018, Jun. Viewpoint-aware Attentive Multi-view Inference for Vehicle Re-identification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA. IEEE.

Zhou, Y. and Shao, L., 2018, Mar. Vehicle Re-identification by Adversarial Bi-directional LSTM Network. IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, USA. IEEE.

Zhou, Y., Liu, L. and Shao, L., 2018. Vehicle Re-identification by Deep Hidden Multi-View Inference. IEEE Transactions on Image Processing. DOI: 10.1109/TIP.2018.2819820

Zhou, Y., Liu, L., Shao, L. and Mellor, M., 2017. Fast Automatic Vehicle Annotation for Urban Traffic Surveillance. IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2017.2740303

Zhou, Y. and Shao, L., 2017, Sep. Cross-View GAN Based Vehicle Generation for Re-identification. Proceedings of the British Machine Vision Conference (BMVC), London, UK. BMVA Press. [Oral, Acceptance rate 5.6%]

Zhou, Y., Liu, L., Shao, L. and Mellor, M., 2016, October. DAVE: a unified framework for fast vehicle detection and annotation. In European Conference on Computer Vision (ECCV) (pp. 278-293), Amsterdam. Springer International Publishing.

Liu, L., Zhou, Y. and Shao, L., 2018. Deep Action Parsing in Videos with Large-scale Synthesized Data. IEEE Transactions on Image Processing. DOI: 10.1109/TIP.2018.2813530 

Liu, L., Zhou, Y. and Shao, L., 2017, May. DAP3D-Net: Where, what and how actions occur in videos? In Robotics and Automation (ICRA), 2017 IEEE International Conference on (pp. 138-145), Singapore. IEEE.


Patents

U.S. Patent No. 16/353,800 [The first inventor], 2019

Title: Medical Images Segmentation and Severity Grading Using Neural Network Architectures with Semi-Supervised Learning Techniques.

U.S. Patent No. 10/176,405 [The first inventor], 2018

Title: Vehicle Re-identification techniques using neural networks for image analysis, viewpoint-aware pattern recognition, and generation of multi-view vehicle representations.


Academic Services

Reviewer for

International Journal of Computer Vision

IEEE Transactions on Image Processing

IEEE Transactions on Medical Imaging

IEEE Transactions on Neural Networks and Learning Systems

IEEE Transactions on Intelligent Transportation Systems

Medical Image Analysis

Neurocomputing

European Conference on Computer Vision (ECCV) 2020

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2021

IEEE Conference on Computer Vision (ICCV) 2019, 2021

International Conference on Medical Imaging Computing and Computer-Assisted Intervention (MICCAI) 2020


Courses

Summer 2021

Computational Vision and Applications (机器视觉与应用,全英文授课,并邀请新加坡、瑞士等国海外专家联合授课)


Contact Information

Email: yizhou@seu.edu.cn, yi.zhou@ieee.org

For highly motivated studentsplease email me with your CV.


More details coming soon...