周毅
职称:副高
所在院系:计算机科学系
研究方向:计算机视觉、机器学习、医学图像分析
电话:
邮箱:yizhou@seu.edu.cn
职务:
个人简介

Dr. Yi Zhou (周毅 Joey)

Ph.D., Associate Professor, IEEE/CCF/CSIG/JSAI Member, Google Scholar

School of Computer Science and Engineering, Southeast University

Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China

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


Brief Bio

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 with the Inception Institute of Artificial Intelligence (IIAI) for three years, Abu Dhabi, United Arab Emirates. He received his 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 and machine learning. He has authored/co-authored 50+ academic papers in top journal/conference such as IEEE TPAMI, IEEE TIP, IEEE TMI, CVPR, ICCV, ECCV, ICLR, AAAI, MICCAI. He has also been granted with several CN/US patents. He has been ranked among the world top 2% scientists in 2023.

周毅,东南大学计算机科学与工程学院副教授,博士生导师,计算机科学系副主任,目前在新一代人工智能技术与交叉应用教育部重点实验室、PALM实验室工作。入选斯坦福全球2%科学家、江苏省“双创博士”、南京市留学择优人才、东南大学“至善青年学者”A层次、东南大学“小米青年学者”、CCF-滴滴“盖亚青年学者”等。2013年至2018年,获全额奖学金,分别赴英国谢菲尔德大学与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、机器学习、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权威的期刊/会议(例如IEEE TPAMI, IEEE TIP, IEEE TMI, CVPR, ICCV, ECCV, ICLR, AAAI, MICCAI等)发表50余篇论文被引4000余次7项中/美发明专利,主持/参与多项国家自然科学基金、江苏省自然科学基金等纵横向项目。学术兼职包括中国视觉与学习青年学者研讨会(VALSE)执行领域主席,医学图像计算青年研讨会(MICS)执行委员,中国图象图形学学会机器视觉专委会执行委员,中国计算机学会计算机视觉、人工智能与模式识别专委会委员,江苏省人工智能学会模式识别、医学图像处理专委会委员,IEEE, CCF, CSIG, JSAI会员,并长期担任十多个国际顶级期刊/会议审稿人。


欢迎对相关研究方向有浓厚兴趣(Self-motivated !!!)、数学优秀、编程能力扎实的同学与我一起工作,每年招收1-2名博士生、5名硕士生左右。

也特别欢迎优秀的本科生跟组进行全面的科研训练。

对拟加入同学的性格期望:勤奋踏实、自律力好、开朗友善。


Contact Info.

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

研究方向

Research Interests

Vision and Language: Data- and Knowledge-Driven Visual Understanding, and Visual Generation (AIGC)

Machine Learning (Deep Learning): Open-World Transfer Learning, Multi-Task Learning, Continual Learning, Multi-Modal Foundation Model and Prompt Engineering

Medical Image Analysis: Medical Disease Diagnosis, Medical Prognosis

教育经历
工作经历
科研项目

Projects and Contest Awards

国家自然科学基金青年项目,面向医学影像多病种诊断的开集域自适应迁移算法研究,2022.01 – 2024.12,主持

江苏省自然科学基金青年项目,面向眼底多病种识别中数据域任务域双偏移的算法研究,2021.07 – 2024.06,主持

南京市留学人员科技创新择优项目,基于开放环境下领域迁移的眼科智能诊断,2023.01 – 2023.12,主持

东南大学至善青年学者资助项目,面向低资源场景医学图像的标记高效学习算法研究,2023.01 – 2025.12,主持

东南大学学科交叉青年特支计划项目,多模态仿生感官融合的智能穿戴系统,2024.07 - 2027.06,子课题负责人

CCF – 滴滴盖亚学者科研基金项目,面向道路场景的多模态多任务基础模型研究,2023.09 – 2024.08,主持

江苏省自然科学基金面上项目,基于深度学习的多模态无监督视频聚类方法研究,2021.07 – 2024.06,参与

腾讯觅影医学人工智能算法大赛,2021.08 – 2021.11,季军

论文著作

Selected Publications

Luo, S., Chen, W., Wu, R., Geng, S., Zhou, Y.*, et al. (2024) Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives. IEEE Transactions on Intelligent Vehicles. DOI: 10.1109/TIV.2024.3406372. [JCR-Q1]

Wu, R., Zhang, C., Zhang, J., Zhou, Y.*, Zhou, T., & Fu, H. (2024) MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise.  In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer Nature Switzerland. [CCF-B]

Xie, Y., Zhou, T., Zhou, Y.*, & Chen, G. (2024) SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer Nature Switzerland. [CCF-B]

Zhao, Y., Zhou, Y., Zhang, Y., Wu, Y., & Zhou, T. (2024) TextPolyp: Point-supervised Polyp Segmentation with Text Cues. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer Nature Switzerland. [CCF-B]

Zhou, T., Zhou, Y., Li, G., Chen, G., & Shen, J. (2024) Uncertainty-aware Hierarchical Aggregation Network for Medical Image Segmentation. IEEE Transactions on Circuits and Systems for Video Technology. DOI:10.1109/TCSVT.2024.3370685. [CCF-B类]

Zhou, T., Zhang, Y., Chen, G., Zhou, Y., Wu, Y., and Fan, D. (2024). Edge-aware feature aggregation network for polyp segmentation. Machine Intelligence Research. http://doi.org/10.1007/s11633-023-1479-8. [JCR-Q1]

Lai, Y., Zhou, Y.*, Liu, X., & Zhou, T. (2024). Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation. In Proceedings of the International Conference on Learning Representations (ICLR)[清华-A类]

Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023). Normalization techniques in training dnns: Methodology, analysis and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8), 10173–10196. [CCF-A]

Liu, X., Zhou, Y.*, Zhou T., & Qin J. (2023). Self-Paced Learning for Open-Set Domain Adaptation[J]. Journal of Computer Research and Development (计算机研究与发展), 60(8): 1711-1726. doi: 10.7544/issn1000-1239.202330210. [CCF-A, Outstanding Paper Award]

Li, Y., Zhou, T., He, K., Zhou, Y., & Shen, D. (2023). Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis. IEEE Transactions on Medical Imaging, 42(11), 3395-3407. [CCF-B]

Zhou, T., Zhou, Y., He, K., Gong, C., Yang, J., Fu, H., & Shen, D. (2023). Cross-level Feature Aggregation Network for Polyp Segmentation. Pattern Recognition, 140, 109555. [CCF-B]

Yang, H., Zhou, T., Zhou, Y., Zhang, Y., & Fu, H. (2023). Flexible Fusion Network for Multi-modal Brain Tumor Segmentation. IEEE Journal of Biomedical and Health Informatics, 27(7), 3349-3359. [JCR-Q1]

Zhou, T., Fan, D., Chen G., Zhou, Y., & Fu, H. (2023). Specificity-preserving RGB-D saliency detection. Computer Visual Media Journal, 9(2), 297-317. [JCR-Q1, Honorable Mention Award]

Zhou, T., Zhou, Y., Gong, C., Yang, J., & Zhang, Y. (2022). Feature aggregation and propagation network for camouflaged object detection. IEEE Transactions on Image Processing, 31, 7036-7047. [CCF-A]

Zhou, H., Huang, Y., Li, Y., Zhou, Y.*, & Zheng, Y. (2022). Blind Super-Resolution of 3D MRI via Unsupervised Domain Transformation. IEEE Journal of Biomedical and Health Informatics, 27(3), 1409-1418. [JCR-Q1]

Zhou, Y., Bai, S., Zhou, T., Zhang, Y., & Fu, H. (2022). Delving into Local Features for Open-Set Domain Adaptation in Fundus Image Analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (pp. 682-692). Cham: Springer Nature Switzerland. [CCF-B]

Huang, L., Zhou, Y., Wang, T., Luo, J., & Liu, X. (2022). Delving into the estimation shift of batch normalization in a network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 763-772).

Zhou, Y., Huang, L., Zhou, T., & Sun, H. (2022). Combating medical noisy labels by disentangled distribution learning and consistency regularization. Future Generation Computer Systems, 141, 567-576. [JCR-Q1]

Zhou, Y., Wang, B., He, X., Cui, S., & Shao, L. (2022). DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images. IEEE Journal of Biomedical and Health Informatics, 26(1), 56-66. [JCR-Q1]

Zhou, Y., Huang, L., Zhou, T., & Shao, L. (2021). CCT-Net: category-invariant cross-domain transfer for medical single-to-multiple disease diagnosis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 8260-8270). [CCF-A]

Zhou, Y., Huang, L., Zhou, T., Fu, H., & Shao, L. (2021). Visual-textual attentive semantic consistency for medical report generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 3985-3994). [CCF-A]

Zhou, T., Fu, H., Chen, G., Zhou, Y., Fan, D. P., & Shao, L. (2021). Specificity-preserving RGB-D saliency detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 4681-4691). [CCF-A]

Zhou, Y., Zhou, T., Zhou, T., Fu, H., Liu, J., & Shao, L. (2021). Contrast-attentive thoracic disease recognition with dual-weighting graph reasoning. IEEE Transactions on Medical Imaging, 40(4), 1196-1206.  [CCF-B]

Huang, L., Zhou, Y., Liu, L., Zhu, F., & Shao, L. (2021). Group whitening: Balancing learning efficiency and representational capacity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 9512-9521). [CCF-A]

Zhou, Y., Huang, L., Zhou, T., & Shao, L. (2021). Many-to-one distribution learning and k-nearest neighbor smoothing for thoracic disease identification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 1, pp. 768-776). [CCF-A]

Li, X., Zhou, T., Li, J., Zhou, Y., & Zhang, Z. (2021). Group-wise semantic mining for weakly supervised semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 3, pp. 1984-1992). [CCF-A]

Zhou, Y., Wang, B., Huang, L., Cui, S., & Shao, L. (2020). A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE Transactions on Medical Imaging, 40(3), 818-828. [CCF-B]

Fan, D. P., Zhou, T., Ji, G. P., Zhou, Y., Chen, G., Fu, H., ... & Shao, L. (2020). Inf-net: Automatic covid-19 lung infection segmentation from ct images. IEEE Transactions on Medical Imaging, 39(8), 2626-2637. [CCF-B]

Huang, L., Zhao, L., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2020). An investigation into the stochasticity of batch whitening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6439-6448). [CCF-A]

Zhou, T., Wang, S., Zhou, Y., Yao, Y., Li, J., & Shao, L. (2020). Motion-attentive transition for zero-shot video object segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 13066-13073). [CCF-A]

Zhou, Y., He, X., Cui, S., Zhu, F., Liu, L., & Shao, L. (2019). High-resolution diabetic retinopathy image synthesis manipulated by grading and lesions. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (pp. 505-513). Cham: Springer International Publishing. [CCF-B]

He, X., Zhou, Y., Wang, B., Cui, S., & Shao, L. (2019). Dme-net: Diabetic macular edema grading by auxiliary task learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (pp. 788-796). Cham: Springer International Publishing. [CCF-B]

Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L. (2019). Collaborative learning of semi-supervised segmentation and classification for medical images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2079-2088). [CCF-A]

Huang, L., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2019). Iterative normalization: Beyond standardization towards efficient whitening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4874-4883). [CCF-A]

Wei, Z., Zhang, J., Liu, L., Zhu, F., Shen, F., Zhou, Y., ... & Shao, L. (2019). Building detail-sensitive semantic segmentation networks with polynomial pooling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 7115-7123). [CCF-A]

Zhou, Y., & Shao, L. (2018). Viewpoint-aware attentive multi-view inference for vehicle re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6489-6498). [CCF-A]

Zhou, Y., & Shao, L. (2018). Vehicle re-identification by adversarial bi-directional lstm network. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 653-662). IEEE. [CCF-C]

Zhou, Y., & Shao, L. (2018). Vehicle re-identification by deep hidden multi-view inference. IEEE Transactions on Image Processing, 27(7), 3275-3287. [CCF-A]

Liu, L., Zhou, Y., & Shao, L. (2018). Deep action parsing in videos with large-scale synthesized data. IEEE Transactions on Image Processing, 27(6), 2869-2882. [CCF-A]

Zhou, Y., Liu, L., Shao, L., & Mellor, M. (2017). Fast automatic vehicle annotation for urban traffic surveillance. IEEE Transactions on Intelligent Transportation Systems, 19(6), 1973-1984. [CCF-B]

Zhou, Y., & Shao, L. (2017). Cross-view GAN based vehicle generation for re-identification. In British Machine Vision Conference (BMVC) (Vol. 1, pp. 1-12). [CCF-C]

Liu, L., Zhou, Y., & Shao, L. (2017). Dap3d-net: Where, what and how actions occur in videos?. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 138-145). IEEE. [CCF-B]

Zhou, Y., Liu, L., Shao, L., & Mellor, M. (2016). DAVE: A unified framework for fast vehicle detection and annotation. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 (pp. 278-293). Springer International Publishing. [CCF-B]


Academic Services

Reviewer / Program Committee

IEEE Transactions on Pattern Analysis and Machine Intelligence

International Journal of Computer Vision

IEEE Transactions on Image Processing

IEEE Transactions on Neural Networks and Learning Systems

Pattern Recognition

IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Medical Imaging

Medical Image Analysis

IEEE Journal of Biomedical and Health Informatics

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020 - 2024

IEEE Conference on Computer Vision (ICCV) 2019 - 2023

European Conference on Computer Vision (ECCV) 2020 – 2024

Conference on Neural Information Processing Systems (NeurIPS) 2023

Association for the Advancement of Artificial Intelligence (AAAI) 2021 – 2024

International Conference on Medical Imaging Computing and Computer Assisted Intervention (MICCAI) 2020 - 2024

专利

Patents

周毅,高铭淇,吴瑞琪. 基于图像文本多模态的眼底图像基础模型预训练方法,国家发明专利,受理时间:2024.04.24,申请号:202410498479.X

周毅,刘星宏一种基于自步学习的开放集图像分类领域自适应方法,国家发明专利,受理时间:2023.04.20,申请号:202310427403.3

肖力行,史瑞潇,周毅面向开放世界目标检测的障碍识别与避障方法及小车系统,国家发明专利,受理时间:2023.09.11,申请号:202311161444.9

黄雅雯,郑冶枫,袁一啸,周毅图像补全方法、装置、设备及存储介质,国家发明专利,受理时间:2022.04.27,申请号:202210457083.1

黄雅雯,郑冶枫,周鹤翔,周毅图像生成模型的训练方法、图像生成方法、装置及设备,国家发明专利,受理时间:2022.04.22,申请号:202210431484.X

Zhou, Yi, et al. Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques. U.S. Patent No. 10,430,946. 1 Oct. 2019.

Zhou, Yi, and Ling Shao. Vehicle re-identification techniques using neural networks for image analysis, viewpoint-aware pattern recognition, and generation of multi-view vehicle representations. U.S. Patent No. 10,176,405. 8 Jan. 2019.

获奖情况

Teaching and Student Training Awards

机器视觉与应用(双语),春季学期

机器视觉与应用(全英文),暑期国际学校(与英国、新加坡高校教师联合授课)

教育部-XX“智能基座”先锋教师

东南大学第30届青年教师授课竞赛,三等奖

东南大学校级学优生优秀指导教师

东南大学校级本科优秀毕业设计(论文)指导教师

中国大学生计算机设计大赛江苏省级赛特等奖,优秀指导教师

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