赵凯
赵凯老师是上海大学通信与信息工程学院副教授。 赵凯老师本科和硕士均毕业于上海大学，硕士导师是沈为 教授 (现上海交通大学教授)； 2020 年博士毕业于 南开大学 ，博士导师是 程明明 教授。 赵老师博士毕业后入选腾讯校招技术大咖（T9级技术专家，腾讯校招最高技术职级），并加入腾讯优图实验室担任高级研究员； 2022年加入加州大学洛杉矶分校从事博士后研究，2025年8月加入上海大学。
赵凯老师的研究领域主要包括计算机视觉、多视角几何、机器学习。 他在计算机视觉和机器学习相关的顶级期刊和会议上发表论文20余篇， 包括 IEEE TPAMI,、CVPR、NeurIPS、ICCV, ECCV 等顶级期刊和会议， 多篇论文入选 ESI 高被引，谷歌学术总引用 5,000 余次。 赵凯老师关于掌纹识别的研究被《麻省理工科技评论》报道， 并应用于微信刷掌支付、北京地铁大兴机场线刷掌入站中。 赵凯老师是很多开源库（例如 PyTorch 和 mmdetection）的活跃贡献者。
联系方式
- 上海大学宝山校区东区12号楼527室
- kzkaizhao(发邮件时请说明来意并尽量简洁直接)
教育背景、任职经历:
- Aug 2025~: 副教授，上海大学。
- Mar 2022~Jul 2025: 博士后，加州大学洛杉矶分校，洛杉矶。
- Oct 2020~Feb 2022: 高级研究员，腾讯优图实验室，上海。
- Sep 2018~Jan 2019: 研究实习生，松下研发中心，新加坡。
- Sep 2017~Jun 2020: 博士生，南开大学，天津。
- Jul 2016~Nov 2016: 研究实习生，腾讯优图实验室，上海。
- Sep 2014~Jun 2017: 硕士生，上海大学，上海。
- Sep 2010~Jun 2014: 本科生，上海大学，上海。
Publications:
- Kai Zhao and Wubang Yuan and Zheng Wang and Guanyi Li and Xiaoqiang Zhu and Deng-ping Fan and Dan Zeng, "Open-Vocabulary Camouflaged Object Segmentation with Cascaded Vision Language Models", Computational Visual Media, 2025. (影响因子=18.3) DOI: 10.26599/CVM.2025.9450512 [PDF] [Google Scholar]
- Kaifeng Pang and Kai Zhao* and Alex Ling Yu Hung and Haoxin Zheng and Ran Yan and Kyunghyun Sung, "NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution", Computers in Biology and Medicine, 2025. (影响因子=7, 中科院 2 区)(* 通讯作者) DOI: 10.1016/j.compbiomed.2024.109354 [PDF] [Google Scholar]
- Kai Zhao and Alex Ling Yu Hung and Kaifeng Pang and Haoxin Zheng and Kyunghyun Sung, "MRI Super-Resolution with Partial Diffusion Models", IEEE Transactions on Medical Imaging, 2024. (影响因子=11.3, 中科院 1 区, Top期刊) DOI: 10.1109/TMI.2024.3483109 [PDF] [Google Scholar]
- Kai Zhao and Zuojie He and Alex Hung and Dan Zeng, "Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction", arXiv:2405.16456, 2024. [Google Scholar]
- Kai Zhao and Kaifeng Pang and Alex LingYu Hung and Haoxin Zheng and Ran Yan and Kyunghyun Sung, "A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging", Cancers, 2024. (影响因子=4.5, 中科院 3 区) DOI: 10.3390/cancers16172983 [PDF] [Google Scholar]
- Alex Ling Yu Hung and Haoxin Zheng and Kai Zhao and Kaifeng Pang and Demetri Terzopoulos and Kyunghyun Sung, "Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection", International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024. [Google Scholar]
- Kai Zhao and Tao Wang and Ruixin Zhang and Wei Shen, "Adaptive feature alignment for adversarial training", Pattern Recognition Letters, 2024. (影响因子=3.9, 中科院 3 区) DOI: j.patrec.2024.10.004 [PDF] [Google Scholar]
- Alex Ling Yu Hung and Kai Zhao and Haoxin Zheng and Ran Yan and Steven S Raman and Demetri Terzopoulos and Kyunghyun Sung, "Med-cDiff: Conditional medical image generation with diffusion models", Bioengineering, 2023. [Google Scholar]
- Yating Xu and Kai Zhao and Liangang Zhang and Mengyao Zhu and Dan Zeng, "Hyperspectral anomaly detection with vision transformer and adversarial refinement", International Journal of Remote Sensing, 2023. [Google Scholar]
- Kai Zhao and Lei Shen and Yingyi Zhang and Chuhan Zhou and Tao Wang and Ruixin Zhang and Shouhong Ding and Wei Jia and Wei Shen, "B'ezierpalm: A free lunch for palmprint recognition", European Conference on Computer Vision, 2022. DOI: 10.1007/978-3-031-19778-9_2 [PDF] [Google Scholar]
- Xuehui Wang and Kai Zhao and Ruixin Zhang and Shouhong Ding and Yan Wang and Wei Shen, "Contrastmask: Contrastive learning to segment every thing", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. [Google Scholar]
- Lei Shen and Yingyi Zhang and Kai Zhao* and Ruixin Zhang and Wei Shen, "Distribution alignment for cross-device palmprint recognition", Pattern Recognition, 2022. (影响因子=7.5, 中科院 1 区, Top期刊)(* 通讯作者) DOI: 10.1016/j.patcog.2022.108942 [PDF] [Google Scholar]
- Jia Li and Junjie Zhang and Fansheng Chen and Kai Zhao and Dan Zeng, "Adaptive material matching for hyperspectral imagery destriping", IEEE Transactions on Geoscience and Remote Sensing, 2022. [Google Scholar]
- Kai Zhao and Xuehui Wang and Xingyu Chen and Ruixin Zhang and Wei Shen, "Rethinking mask heads for partially supervised instance segmentation", Neurocomputing, 2022. (影响因子=5.5, 中科院 2 区) DOI: 10.1016/j.neucom.2022.10.003 [PDF] [Google Scholar]
- Kai Zhao and Qi Han and Chang-Bin Zhang and Jun Xu and Ming-Ming Cheng, "Deep hough transform for semantic line detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. (影响因子=20.8, 中科院 1 区, Top期刊) DOI: 10.1109/TPAMI.2021.3077129 [PDF] [Google Scholar]
- Shang-Hua Gao and Ming-Ming Cheng and Kai Zhao and Xin-Yu Zhang and Ming-Hsuan Yang and Philip Torr, "Res2net: A new multi-scale backbone architecture", IEEE transactions on pattern analysis and machine intelligence, 2019. (影响因子=20.8, 中科院 1 区, Top期刊) [Google Scholar]
- Kai Zhao and Shanghua Gao and Wenguan Wang and Ming-Ming Cheng, "Optimizing the F-measure for threshold-free salient object detection", Proceedings of the IEEE/CVF international conference on computer vision, 2019. [PDF] [Google Scholar]
- Kai Zhao and Jingyi Xu and Ming-Ming Cheng, "Regularface: Deep face recognition via exclusive regularization", Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019. [PDF] [Google Scholar]
- Wei Shen and Yilu Guo and Yan Wang and Kai Zhao and Bo Wang and Alan Yuille, "Deep differentiable random forests for age estimation", IEEE transactions on pattern analysis and machine intelligence, 2019. (影响因子=20.8, 中科院 1 区, Top期刊) [Google Scholar]
- Kai Zhao and Wei Shen and Shanghua Gao and Dandan Li and Ming-Ming Cheng, "Hi-Fi: Hierarchical Feature Integration for Skeleton Detection", Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018. DOI: 10.24963/ijcai.2018/166 [PDF] [Google Scholar]
- Wei Shen and Yilu Guo and Yan Wang and Kai Zhao and Bo Wang and Alan L Yuille, "Deep regression forests for age estimation", Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. [Google Scholar]
- Wei Shen and Kai Zhao and Yuan Jiang and Yan Wang and Xiang Bai and Alan Yuille, "Deepskeleton: Learning multi-task scale-associated deep side outputs for object skeleton extraction in natural images", IEEE Transactions on Image Processing, 2017. [Google Scholar]
- Wei Shen and Kai Zhao and Yilu Guo and Alan L Yuille, "Label distribution learning forests", Advances in neural information processing systems, 2017. [Google Scholar]
- Wei Shen and Kai Zhao and Yuan Jiang and Yan Wang and Zhijiang Zhang and Xiang Bai, "Object skeleton extraction in natural images by fusing scale-associated deep side outputs", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. [Google Scholar]
The list is generated from publications.bib. Please visit my Google Scholar for full publication list.
Academic Services:
- 会议审稿: CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, AISTAT, ACCV
- 期刊审稿: IEEE TPAMI, IEEE TIP, IEEE TNNLS, IEEE TMM, Pattern Recognition
- Guest Editor: MDPI Diagnostics
研究生培养
入组与学习建议
- 本校保研同学原则上应自大四学年开始即加入实验室，期间不建议外出实习；毕业设计须在实验室完成，并鼓励提前进组参与科研项目及相关课程学习；
- 外校保研同学如条件允许，毕业设计亦建议在实验室完成，或选择与实验室研究方向相关的课题；原则上须于大四暑期到校报到（实验室可协调解决暑期住宿），并参与实验室科研项目；
- 考研学生在复试结束后可联系实验室参加组内面试，经面试通过并确立入组意向者，与外校保研同学相同，于大四暑期到校报到并参与实验室科研工作。
相关课程
所有研究生 必须 在入学第一学年结束前完成以下在线课程的学习，通过考核后方可开展科研和学术论文写作。
- 斯坦福-CS231n 深度学习和计算机视觉（b站搬运、中文讲解）， 课程中的 Python numpy矩阵编程教程 也很重要，要跟着教程实践必修
- 斯坦福 CS229 机器学习（2018年秋季），（b站搬运）必修
- 麻省理工学院-矩阵方法及其在信号处理和机器学习中的应用必修
- 麻省理工学院-计算机科学教育中缺失的一课（中译版）。 这门课边学边在实践中应用，重点了解“ Shell Tools and Scripting，Command-line Environment，Version Control (Git)，Debugging and Profiling，Metaprogramming”这几个章节部分章节必修
- 斯坦福 CS234: 强化学习选修
对于申请阶段的保研和考研学生，如能提前修读并通过相关课程（尤其是 CS231n、CS229），将在导师遴选时获得优先考虑，该课程内容也是面试的重要参考。
