Ming-Ming Cheng

Ming-Ming Cheng is a professor at the College of Computer Science, Nankai University, leading the Media Computing Lab. He received his Ph.D. from Tsinghua University in 2012 and then worked with Prof. Philip Torr in Oxford for two years. His research interests include computer vision and computer graphics. He has published over 100 papers in leading journals and conferences, such as IEEE TPAMI, ACM TOG, IEEE CVPR, etc. Many of his algorithms have become quite popular in the community, receiving more than 40,000+ paper citations. He received several research awards, including the National Science Foundation for Distinguished Young Scholars of China, the ACM China Rising Star Award, the IBM Global SUR award, etc. He is a senior member of the IEEE and on the editorial boards of the IEEE TPAMI and IEEE TIP. [CV]

程明明，南开大学杰出教授，新一代人工智能发展战略研究院副院长。主持承担了国家杰出青年科学基金、优秀青年科学基金项目、科技部重大项目课题等。他的主要研究方向是人工智能、计算机视觉和计算机图形学，在SCI一区/CCF A类刊物上发表学术论文100余篇（含IEEE TPAMI论文38篇），h-index为97，论文谷歌引用5.7万余次，单篇最高引用5千余次，多次入选全球高被引科学家和中国高被引学者。技术成果被应用于华为、国家减灾中心等多个单位的旗舰产品。获得教育部自然科学一等奖2项、其他省部级科技奖2项。培养的3名博士生获得省部级优秀博士论文奖。现担任中国图象图形学学会副秘书长、天津市人工智能学会副理事长和顶级期刊IEEE TPAMI, IEEE TIP和《中国科学：信息科学》编委。[简历]

招收博士、硕士研究生，来信前请务必阅读：关于研究生招生

Research

I’m currently working on image scene analysis, editing, and retrieval. These works are mainly in the following aspects: (I) biologically motivated salient region detection and segmentation; (II) sketch-based image retrieval and composition; (III) interactive image analysis and manipulation; (IV) similar scene elements analysis for smart image manipulation. These works tried to recover parts of scene object-level information from images according to biological inspiration or with the help of simple user assistance in sketch form. Such scene object-level information includes one or more parts of the following aspects: the object of interest regions, object correspondence, region layering, symmetry, repetition, and 3D relations. Ideally, we expect automatic extraction of full 3D information, category names, attributes, and object relations about the underlying image scene for intelligent image understanding, manipulation, organization, and retrieval. [Research galleries]

Selected Publications

My publications can be found here. See also: DBLP, Google, Scopus, arXiv, Publons. Here are some recent publications.

Large-scale Unsupervised Semantic Segmentation, Shanghua Gao, Zhong-Yu Li, Ming-Hsuan Yang, Ming-Ming Cheng*, Junwei Han, Philip Torr, IEEE TPAMI, 45(6):7457-7476, 2023. [pdf | code | bib | 中译版]
A Highly Efficient Model to Study the Semantics of Salient Object Detection, Ming-Ming Cheng^*#, Shanghua Gao^#, Ali Borji, Yong-Qiang Tan, Zheng Lin, Meng Wang, IEEE TPAMI, 2022. [pdf | bib | project | code | 中译版]
Structure-measure: A New Way to Evaluate Foreground Maps, Ming-Ming Cheng^*, Deng-Ping Fan, IJCV,129(9):2622-2638, 2021. [pdf | code | bib |project | 中译版]
Res2Net: A New Multi-scale Backbone Architecture, Shanghua Gao#, Ming-Ming Cheng^*#, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr, IEEE TPAMI, 43(2):652-662, 2021. [pdf | code | project |PPT | bib | 中译版]
Richer Convolutional Features for Edge Detection, Yun Liu, Ming-Ming Cheng^*, Xiaowei Hu, Jia-Wang Bian, Le Zhang, Xiang Bai, Jinhui Tang, IEEE TPAMI, 41(8):1939-1946, 2019. [pdf|project|bib|code|中译版]
Deeply supervised salient object detection with short connections, Qibin Hou, Ming-Ming Cheng^*, Xiaowei Hu, Ali Borji, Zhuowen Tu, Philip Torr, IEEE TPAMI, 41(4):815-828, 2019. [pdf|project|bib|code]
Structure-Preserving Neural Style Transfer, Ming-Ming Cheng^*#, Xiao-Chang Liu^#, Jie Wang, Shao-Ping Lu, Yu-Kun Lai, Paul L. Rosin, IEEE TIP, 29:909-920, 2020. [pdf | bib | project | code]
Shifting More Attention to Video Salient Object Detection, Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng^*, Jianbing Shen, IEEE CVPR (Oral & Best Paper Finalist), 2019. [pdf|bib|中译版|code|project]
互联网图像驱动的语义分割自主学习, 侯淇彬, 韩凌昊, 刘姜江, 程明明^*, 中国科学：信息科学, 2021. [pdf | bib | project]
认知规律启发的物体分割评价标准及损失函数，范登平, 季葛鹏, 秦雪彬, 程明明^*, 中国科学：信息科学, 2021. [ pdf | code | bib ]

My Colleagues

We are looking forward to having elegant students or researchers join us. Positions for Master’s, Ph.D., and post-doc are opening. If you are interested in our research and want to join us, please send your CV (maximum 2 pages) and grades to cmm_AT_nankai.edu.cn.

Research Collaborators (partial)

We are collaborating with leading scientists and researchers worldwide, with whom many highly influential pieces of research have been made possible. We encourage faculties and students to continue such collaborations by doing joint research and/or physically visiting these collaborators.