About Me

Hi, I am Yinghuan. I received my B.Sc. and Ph.D. degree both in Computer Science at Nanjing University in 2007 and 2013, respectively. Currently, I am an Associate Professor in the Department of Computer Science and Technology at Nanjing University, and a member of the National Key Laboratory for Novel Software Technology. Also, I am a member of the Reasoning and Learning Research Group, led by Prof. Yang Gao.

In 2011-2012, I visited the IDEA Lab of the University of North Carolina at Chapel Hill (UNC) under the support from the China Scholarship Council (CSC), and worked with Prof. Dinggang Shen. In 2016, I returned to the IDEA Lab for a three months visiting. Also, I spent two months in 2014 as a Visiting Scholar at the Data Science Lab of the University of Technology, Sydney (UTS), working with Prof. Longbing Cao.

Currently, our interests lie broadly in machine learning, pattern recognition, computer vision, and medical image analysis.

Specifically, we have been focusing on developing theories, models, and methods for semi-supervised learning, weakly-supervised learning, domain adaptation, and domain generalization. Our goal is to create efficient and effective solutions to these challenges. We have also been developing machine learning-based tools to help address data analysis problems in medical imaging and clinical data. We hope that these tools can contribute towards building real-world systems for use in clinical scenarios.

Chinese Version (中文简介)

News

• 目前课题组招收2024年入学研究生名额已经用完, 感谢各位同学对课题组的关注.

• [Sept, 2023] Three papers have been accepted by TOMM, TPAMI, and NeurIPS 2023.

• [July, 2023] We have 7 papers accepted by ICCV 2023. Congratulations to Jian, Zekun, Jintao, Lihe, Yue, Guan, Xiran and all the collaborators.

• [May, 2023] Congratulations to Lihe and Ziqi for Excellent Master Thesis (Nominee) of Department of Computer Science and Technology of Nanjing University. (祝贺丽鹤、子奇获得2023年度南京大学计算机科学与技术系优秀硕士论文提名)

• [May, 2023] To serve as Area Chair of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

• [Apr, 2023] 获评中国人工智能学会—华为 MindSpore 学术奖励基金优秀结题项目.

• [Feb, 2023] Three papers about DG Classification, Semi-supervised Learning and Barely-supervised Medical Image Segmentation have been accepted by CVPR 2023.

• [Sep, 2022] One paper about Barely Supervised Medical Image Segmentation has been accepted by TMI.

• [Sep, 2022] One paper about Barely-supervised Learning has been accepted by NeurIPS 2022.

• [Mar, 2022] 智能重症可解释评估模型 TSOFA 已上线（ A Time-Incorporated SOFA-Based Explainable Machine Learning Model for Mortality Prediction）与中国人民解放军东部战区总院重症医学科合作

• [Mar, 2022] Three papers about Semi-supervised Learning and DG Segmentation are accepted by CVPR 2022.

Recent Publication

• Jian Zhang, Lei Qi, Yinghuan Shi, Yang Gao. “ DomainAdaptor: A Novel Approach to Test-time Adaptation. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023. [Code]

• Zekun Li, Lei Qi, Yinghuan Shi, Yang Gao. “ IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Accepted as Oral). [Code]

• Jintao Guo, Lei Qi, Yinghuan Shi. “ DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023. [Code]

• Lihe Yang, Zhen Zhao, Lei Qi, Yu Qiao, Yinghuan Shi, Hengshuang Zhao. “ Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023. [Code]

• Yue Duan, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, Yinghuan Shi. “ Class Transition Tracking Based Pseudo-Rectifying Guidance for Semi-supervised Learning with Non-random Missing Labels. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023. [Code]

• Guan Gui, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, Yinghuan Shi. “ Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Accepted as Oral). [Code]

• Xiran Wang, Jian Zhang, Lei Qi, Yinghuan Shi. “ Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization. ” IEEE/CVF International Conference on Computer Vision (ICCV), 2023. [Code]

• Heng Cai, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao. “ 3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks. ” Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023 (Early Acceptance). [Code]

• Jintao Guo, Na Wang, Lei Qi, Yinghuan Shi. “ ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-Frequency Transform for Domain Generalization. ” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Code]

• Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi. “ Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation. ” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Code]

• Heng Cai, Shumeng Li, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao. “ Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation. ” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [Code]

• Yue Duan, Zhen Zhao, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi, Yang Gao. “ MutexMatch: Semi-supervised Learning with Mutex-based Consistency Regularization. ” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023. [Code]

More Papers

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