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.
Major Honors & Awards
- 2023: 视觉媒体的层次化内容感知,教育部自然科学一等奖,赵耀,程明明,魏云超,侯淇彬,韦世奎
- 2020: 图像场景理解与内容敏感图像处理,吴文俊人工智能科学技术奖自然科学二等奖,程明明,侯淇彬,杨巨峰,范登平,刘云
- 2019: 弱监督条件下的图像语义分割研究,中国图象图形学学会科学技术奖一等奖,赵耀,程明明,魏云超
- 2019: 天津市中青年科技创新领军人才
- 2019: 天津市青年科技奖
- 2016年度国家“万人计划”青拔
- 2016: ACM中国新星奖
- 2015: 中科协青年人才托举计划
- 2013: 北京市优秀博士论文奖
- 2013: 可视媒体几何计算的理论与方法,教育部自然科学一等奖,胡事民、黄继武、艾海舟、陈韬、黄畅、程明明、来煜昆、毕宁、项世军
Ph.D. Students
Master Students
Alumni
Academic Service
- Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (Oct. 2021 ~), IEEE Transactions on Image Processing (TIP) (Oct. 2018 ~ ), Machine Intelligence Research (Apr. 2021 ~), 《中国科学:信息科学》 (2022年12月~)
- Area Chair of IEEE CVPR 2019, 2021, 2023, ICCV 2019, 2025, NeurIPS 2022, 2024
- SPC: AAAI 2020, 2022, 2024, IJCAI 2021
- General Chair of VALSE 2023.
- Program Chair of VALSE 2021, VALSE 2016
- Organizing Committee Chair of ICIG 2021
- Program Chair of Chinese Conference on Computer Vision (CCCV) 2017
- Organization Chair of Computational Visual Media (CVM) 2017.
- Program Chair: VALSE 2016
- 中国图象图形学学会副秘书长,2016.8~
- 天津市人工智能学会副理事长,2021.4~
Links
- Closely collaborated research Groups: CG Tsinghua @ Beijing, Torr Vision Group @ Oxford, VGG Group @ Oxford, MSRC I3D Group @ Cambridge,
- Cooperators: Shi-Min Hu, Philip Torr, Niloy J. Mitra, Shahram Izadi, Carsten Rother, Jamie Shotton, Pushmeet Kohli, Ping Tan, Ariel Shamir, Xiaolei Huang
- Useful Resources: Computer Graphics Resource, Computer Graphics, Computer Vision, CV Resource, CV Datasets, VALSE, Most cited papers in Computer Vision, The word clock, Submitting to PAMI, AceRankings, 手写字体, ImportantCitations, PDF2PPT, 知网毕设系统
- Recommend Journals from China: Computational Visual Media, Science China: Information Science, Machine Intelligence Research, Visual Intelligence.
Hello Professor Cheng,
I recently read your paper (SANet: A Slice-Aware Network for Pulmonary Nodule Detection) and was impressed by it. I’m interested in researching lung cancer detection with CT image data and was wondering if you could provide me with the PN9 dataset you used. I have tried reaching out to the co-author, Mei Jie, for the dataset, according to the website instructions but haven’t heard back, so I just wanted to check with you if I could get some help with that.
Thank you very much
程老师, 您好,我最近在做视频显著性检测的相关工作,发现您的网址:https://mmcheng.net/davsod/,对视频显著性做了详细的解释,但是网址上相关的图片都失效了,无法显示,能否给我分享下相关的图片和视频呢?(wjx514051264@126.com)
[…] Zheng Lin , Zheng-Peng Duan, Zhao Zhang , Chun-Le Guo, Ming-Ming Cheng, […]
程老师您好!读了您2015年的《Global Contrast Based Salient Region Detection》,请问下在哪可以找到相关程序代码呢?如果您方便可否麻烦您发至我的邮箱,或者提供下载地址呢?
这里有,好几个论文的代码都在一个C++ project里面:https://github.com/MingMingCheng/CmCode/
程老师您好!想要请教一些关于Video Salient Object Detection (VSOD)以及Eye Fixation Prediction(EFP)的问题。(若有什么理解或表达错误的地方,恳请老师指正。)
(1)我看到一些采用半监督方法做VSOD的工作,但是,目前没有调研到采用半监督方法做EFP的工作,请问采用半监督做EFP,是不是有什么不合理的地方,或者是有哪些难点目前没办法解决?
(2)在DAVSOD数据集中,我发现少数fixation_map中标注的点,并不在GT_object标注的那个轮廓线内,所以,我就在想,对于Video Salient Object和Eye Fixation,人眼所关注的东西,是不是有些差别?还是说忽略了些什么东西?
祝好!
杜丽娜(Dulina@emails.bjut.edu.cn)
SOD其实是有一些先验的。参考我2015年的PAMI论文,仅用对比度先验(无需监督信息)就可以得到很不错的SOD结果。因此,半监督的去做VSOD本质上并不困难。但是Fixation就很难了。Fixation的主观性太强,gt的一致性也要低很多。因此采用半监督的方法弄起来就要困难很多。
老师您好,看了2019的cpfp后想索要一份该模型在SIP数据集下的结果图片集,不知道能不能分享一下。
数据和结果都在项目主页上 http://dpfan.net/d3netbenchmark/
建议由具体问题也在具体paper页面下方留言
老师您好,看完您发表的文章《Structure-Preserving Neural Style Transfer》后,我有一些疑问,希望老师在繁忙工作之余,帮我解惑一下,万分感谢。
1,训练的时,生成网络的输入只有X_content, 是不是每次训练后的网络只能生成特特定style的图片。
2,训练时将style的图片和content的图片同时输入到网络中,会不会使网络有更好的拓展性。
这个工作主要是研究如何保持结构。后续有很多工作支持同时输入style和content
程老师,您的网课《论文写作指导》链接失效,可否更新一下链接?
我登录腾讯课堂,好像后台也显示已经失效。没找到怎么恢复。估计只能以后重新录制了。
程老师,请问2021 CVPR 还会有 Learning from Imperfect Data Challenge and Workshop 吗?
还会有
程老师,您好,我看到您网站的一篇2020eccv的工作,Gradient-Induced Co-Saliency Detection,我看到这篇文章提出的拼图训练策略想实现一下,但不是很理解,发现提供的代码没有train部分,可以分享一下嘛
您好,已经发送给您。
老师您好,我是澳门大学的研究生,想在您的基础上继续研究Semantic Edge Detection算法,我承诺不将代码或基于您的代码的研究成果商用,请问可以发我一份Semantic Edge Detection with Diverse Deep Supervision训练代码嘛?
您好,因为这篇论文仍然在审稿状态(两年了),所以现阶段不方便共享代码。一旦论文被接收,我们一定第一时间开源代码。
程老师您好,有幸拜读了您主页publications里展示的2020PAMI弱监督实例分割论文《Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation》,所取得的结果令人印象深刻,请问您能否繁忙的工作之余,分享一下实验所用的代码,十分感激!!!
留意到github上在1年前已经有这篇文章的项目,但是目前只有Code is coming soon…
你好。谢谢提醒。论文录用的时候刚好碰上近期cvpr deadline,没时间快速整理。我们计划cvpr deadline之后整理一下放出来。
老师您好,有幸拜读了您发表的文章《Structure-Preserving Neural Style Transfer》,对其应用在所取得的结果令人印象深刻,请问您能否繁忙的工作之余,分享一下在实验所用的代码,十分感激!!!
谢谢提醒,我们尽快准备。当时做这个项目的学生毕业了,所以进度有点慢。