周毅 - 东南大学 - 计算机科学与工程学院

个人简介

Dr. Yi Zhou (周毅 Joey) Ph.D., Associate Professor, IEEE Senior Member, MICCAI/CCF/CSIG 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 MLLM, AI4Healthcare, and Embodied AI. He has authored/co-authored 60+ 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 since 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：多模态大模型、AI4Healthcare、具身智能等。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 60 余篇论文，被引6 000 余次，8 项中 / 美发明专利，主持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高级会员，MICCAI/CCF会员等，并长期担任十多个国际顶级期刊 / 会议审稿人。

研究领域

多模态大模型 AI4Healthcare 具身智能 Vision and Language: Vision-Language Pre-training, Multi-modal Large Language Model, Embodied AI Machine Learning (Deep Learning): Open-World Transfer Learning, Continual/Incremental Learning AI4Healthcare: Agentic LLMs in Healthcare, Medical Image Analysis

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

1. Chen, W., and Zhou, Y.* (2025) Make Domain Shift a Catastrophic Forgetting Alleviator in Class-Incremental Learning. In Proceedings of the AAAI Conference on Artificial Intelligence. 2. Yao, Y., Wu, R., Zhou, Y. * , & Zhou, T. (2025) Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities. In International Conference on Medical Image Computing and Computer-Assisted Intervention ( MICCAI ). Cham: Springer Nature Switzerland. 3. Huang, K., Zhou, Y., Fu, H., Zhang, Y., Gong, C., & Zhou, T. (2025) Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention ( MICCAI ). Cham: Springer Nature Switzerland. 4. Hu, J., Zhou, T., Huang, K., Zhou, Y. , Zhang, H., Fan, Bo., & Fu, H. (2025) Uncertainty-guided Prototype Reliability Enhancement Network for Few-shot Medical Image Segmentation. IEEE Transactions on Medical Imaging , DOI: 10.1109/TMI.2025.3621452. 5. Ni, Z., Fu, A., Zhou, Y.* (2025) FREAK: Frequency-moduled High-fidelity and Real-time Audio-driven Talking Portrait Synthesis. In ACM International Conference on Multimedia Retrieval ( ICMR ). 6. Fu, A., Ni, Z., Zhou, Y.* (2025) Dual Audio-Centric Modality Coupling for Talking Head Generation. In International Conference on Virtual Reality and Visualization ( ICVRV ). 7. Huang, K., Zhou, T., Fu., H., Zhang, Y., Zhou, Y. , Gong, C., & Liang, D. (2025) Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging , DOI: 10.1109/TMI.2025.3530097. 8. Zhou, T., Fu, H., Zhang, Y., Zhou, Y. , & Wu, X. (2025) Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation. IEEE Transactions on Image Processing , DOI: 10.1109/TIP.2025.3599783. 9. Lu, Z., Zhang, Y., Zhou, Y. , Wu, Y., & Zhou, T. (2024) Domain-Interactive Contrastive Learning and Prototype-Guided Self-Training for Cross-Domain Polyp Segmentation. IEEE Transactions on Medical Imaging , DOI: 10.1109/TMI.2024.3443262. 10. 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. 11. 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. 12. 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. 13. 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. 14. 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. 15. Gu, Y., Zhou, T., Zhang, Y., Zhou, Y. , He, K., Gong, C., and Fu, H. (2024). Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation. Pattern Recognition , DOI: 10.1016/j.patcog.2024.110962. 16. 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. 17. 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) . 18. 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. 19. 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. [ Outstanding Paper Award] 20. 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. 21. 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. 22. 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. 23. 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. [ Honorable Mention Award ] 24. 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. 25. 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. 26. 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. 27. 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). 28. 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. 29. 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. 30. 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). 31. 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). 32. 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). 33. 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. 34. 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). 35. 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). 36. 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). 37. 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. 38. 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. 39. 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). 40. 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). 41. 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. 42. 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. 43. 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). 44. 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). 45. 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). 46. 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). 47. 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. 48. Zhou, Y. , & Shao, L. (2018). Vehicle re-identification by deep hidden multi-view inference. IEEE Transactions on Image Processing , 27(7), 3275-3287. 49. 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. 50. 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. 51. 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). 52. 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. 53. 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.

学术兼职

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高级会员 MICCAI/CCF会员长期担任十多个国际顶级期刊 / 会议审稿人