Mingming Gong
The University of Melbourne, University of Technology Sydney
Research areas: Computer Science · Computer Vision and Pattern Recognition · Domain Adaptation and Few-Shot Learning · Advanced Neural Network Applications · Generative Adversarial Networks and Image Synthesis
0000-0001-7147-5589
About
I am an associate professor in Data Science at the School of Mathematics and Statistics, the University of Melbourne (UoM), and an affiliated associate professor of Machine Learning at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). I lead the Machine Learning and Reasoning (MLR) group. Before joining UoM, I was a postdoctoral research fellow at University of Pittsburgh and Carnegie Mellon University. I obtained my PhD from University of Technology Sydney, Master degree from Huazhong University of Science and Technology, and bachelor's degree from Nanjing University. From 03/2013 - 10/2013, I was a research intern at Max-Planck Institute for Intelligent Systems.
More information about him can be found in https://mingming-gong.github.io/.
Employment
-
University of Melbourne Lecturer2019 - Present
Education
- 2012 - 2017
Projects & Funding
Projects & funding information is unavailable.
Publications (50)
- A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning Save
- A Unified B-Spline Framework for Scale-Invariant Keypoint Detection Save
- Adversarial Robustness through the Lens of Causality Save
- Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint Save
- CRIS: CLIP-Driven Referring Image Segmentation Save
- Exploring Set Similarity for Dense Self-supervised Representation Learning Save
- Fair Classification with Instance-dependent Label Noise Save
- Few-Shot Font Generation by Learning Fine-Grained Local Styles Save
- Learning multi-level weight-centric features for few-shot learning Save
- Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation Save
- Rethinking Class-Prior Estimation for Positive-Unlabeled Learning Save
- Sample selection with uncertainty of losses for learning with noisy labels Save
- Uncertainty-aware clustering for unsupervised domain adaptive object re-identification Save
- 3d-future: 3d furniture shape with texture Save
- Class2simi: A noise reduction perspective on learning with noisy labels Save
- Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? Save
- Dynamic Joint Distribution Alignment Network for Bearing Fault Diagnosis Under Variable Working Conditions Save
- Federated Causal Discovery Save
- HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking Save
- Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach Save