个人简介
沈成超，现为中南大学计算机学院特聘副教授（数据科学与工程系）、硕士生导师（个人主页），湖南省人工智能学会委员、青工委副秘书长。主要从事计算机视觉研究，涉及无监督/自监督学习、小样本学习、联邦学习、知识迁移、模型压缩、模型可解释性等，近五年发表了包括CVPR、ICCV、ECCV、AAAI、NeurIPS、IJCAI等10余篇国际计算机视觉/人工智能顶会论文，并在Google Scholar上被引用1000余次，曾担任了CVPR、ICCV、AAAI、NeurIPS、ICML、ICLR、IJCAI等国际顶会论文审稿人，TPAMI、TIP、TNNLS等国际知名期刊论文审稿人，主持国家自然科学基金、湖南省自然科学基金、长沙市自然科学基金各一项。
【讲授课程】①计算机组成原理与汇编（必修课，64课时，秋冬学期，本科生）②计算机视觉前沿技术（选修课，32课时，秋冬学期，本科生）
【招生信息】课题组长期招收研究生（包括：计算机科学与技术、电子信息），欢迎对热爱计算机视觉研究、有事业心的学生加入课题组，希望你有学术追求。课题组能保证科研所需的实验条件，并提供细致的科研指导和充足的助研津贴。
【联系方式】scc.cs@csu.edu.cn
【相关链接】[Homepage] [Google Scholar] [DBLP] [Github] [Zhihu]
代表论文
[1] Hao Tang, Dawei Liu, Chengchao Shen*. Data-Efficient Multi-Scale Fusion Vision Transformer. Pattern Recognition (中科院一区, IF=7.5), 2025. [paper] [arXiv] [code]
[1] Chengchao Shen, Jianzhong Chen, Jianxin Wang. Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning. Arxiv, 2024. [arXiv] [code] [blog]
[1] Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang. Inter-Instance Similarity Modeling for Contrastive Learning. Arxiv, 2023. [arXiv] [code] [blog]
[1] Chengchao Shen, Jianzhong Chen, Shu Wang, Hulin Kuang, Jin Liu, Jianxin Wang. Asymmetric Patch Sampling for Contrastive Learning. Pattern Recognition (中科院一区, IF=7.5), 2025. [paper] [arXiv] [code]
[1] Tao Sheng, Chengchao Shen*, Yuan Liu, Yeyu Ou, Zhe Qu, Yixiong Liang, Jianxin Wang. Modeling Global Distribution for Federated Learning with Label Distribution Skew. Pattern Recognition (中科院一区, IF=8.5), 2023. [paper] [arXiv] [code]
[2] Zhe Qu, Xingyu Li, Xiao Han, Rui Duan, Chengchao Shen, Lixing Chen. How To Prevent the Poor Performance Clients for Personalized Federated Learning. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, CCF A), 2023.
[3] Chengchao Shen, Youtan Yin, Xinchao Wang, Xubin Li, Jie Song, Mingli Song. Training Generative Adversarial Networks in One Stage. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, CCF A), 2021.
[4] Chengchao Shen, Xinchao Wang, Youtan Yin, Jie Song, Sihui Luo, Mingli Song. Progressive Network Grafting for Few-Shot Knowledge Distillation. AAAI Conference on Artificial Intelligence (AAAI, CCF A), 2021.
[5] Chengchao Shen, Mengqi Xue, Xinchao Wang, Jie Song, Li Sun, Mingli Song. Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation. The IEEE International Conference on Computer Vision (ICCV, CCF A), 2019.
[6] Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, Mingli Song. Amalgamating Knowledge towards Comprehensive Classification. AAAI Conference on Artificial Intelligence (AAAI, CCF A), 2019.
[7] Chengchao Shen, Jie Song, Sihui Luo, Li Sun, Mingli Song. Intra-class Structure Aware Networks for Screen Defect Detection. International Conference on Neural Information Processing (ICONIP, CCF C), 2018.
[8] Jie Song, Chengchao Shen, Jie Lei, An-Xiang Zeng, Kairi Ou, Dacheng Tao, Mingli Song. Selective Zero-Shot Classification With Augmented Attributes. The European Conference on Computer Vision (ECCV, CCF B), 2018.
[9] Jie Song, Chengchao Shen, Yezhou Yang, Yang Liu, Mingli Song. Transductive Unbiased Embedding for Zero-Shot Learning. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, CCF A), 2018.
[10] Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, Mingli Song. Contrastive Model Inversion for Data-Free Knowledge Distillation. International Joint Conference on Artificial Intelligence (IJCAI, CCF A), 2021.
[11] Gongfan Fang, Yifan Bao, Jie Song, Xinchao Wang, Donglin Xie, Chengchao Shen, Mingli Song. Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data. Advances in Neural Information Processing Systems (NeurIPS, CCF A), 2021.
[12] Jie Song, Yixin Chen, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, Mingli Song. DEPARA: Deep Attribution Graph for Deep Knowledge Transferability. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, CCF A), 2020.
[13] Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song. Deep Model Transferability from Attribution Maps. Advances in Neural Information Processing Systems (NeurIPS, CCF A), 2019.
学术服务
会议审稿
• The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• The IEEE International Conference on Computer Vision (ICCV)
• The European Conference on Computer Vision (ECCV)
• AAAI Conference on Artificial Intelligence (AAAI)
• Advances in Neural Information Processing Systems (NeurIPS)
• International Conference on Learning Representations (ICLR)
• International Conference on Machine Learning (ICML)
• International Joint Conference on Artificial Intelligence (IJCAI)
• The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)
• The European Conference on Artificial Intelligence (ECAI)
期刊审稿
• IEEE Transactions on Image Processing (TIP)
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• Machine Learning