Dinghao YANG   杨丁豪

Algorithm Engineer, Alibaba Cloud

Machine Learning Platform for AI (PAI)
Hangzhou, China

Email: yangdinghao.ydh@alibaba-inc.com;
Google Scholar: Google Scholar Link
Github: https://github.com/Dinghow
Personal Blog: https://dinghow.site/dinghow-blog


I am an algorithm engineer at Machine Learning Platform for AI, Alibaba Cloud. I received the M.S degree from the School of Electronic and Computer Engineering, Peking University, in 2023, and the B.E. degree from the School of Software Engineerning, Tongji University, in 2020. I used to be a research intern at Data and Computing Platform, SenseTime Research, during 2020-2022, supervised by Bin Wang and Weijia Li.

My research interests include point cloud analysis, image segmentation/matting, and inference optimization for neural network.

You can find my Curriculum Vitae here.



Preprint Papers

PointCHD: A Point Cloud Benchmark for Congenital Heart Disease Classification and Segmentation
Dinghao Yang, Wei Gao
Under Review
[PDF] [Code]
Unified Interactive Image Matting
Dinghao Yang, Bin Wang, Weijia Li, Yiqi Lin, Conghui He
Under Review
[PDF] [Code]

Published Papers

Exploring the User Guidance for More Accurate Building Segmentation from High-Resolution Remote Sensing Images
Dinghao Yang, Bin Wang, Conghui He, Weijia Li
International Journal of Applied Earth Observation and Geoinformation (JAG)
[PDF] [Code]
Exploiting Manifold Feature Representation for Efficient Classification of 3D Point Clouds
Dinghao Yang, Wei Gao, Ge Li, Hui Yuan, Junhui Hou, Sam Kwong
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM)
[PDF] [Code]
Cycle-Consistent Learning for Weakly Supervised Semantic Segmentation
Bin Wang, Yu Qiao, Dahua Lin, Dinghao Yang, Weijia Li
The 3rd International Workshop on Human-Centric Multimedia Analysis (HUMA), 2022
Low-rate image compression with super-resolution learning
Wei Gao, Lvfang Tao, Linjie Zhou, Dinghao Yang, Xiaoyu Zhang, Zixuan Guo
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2020


Intelligent Annotation for SenseBee
I am principally responsible for the interactive data annotation algorithms for SenseBee, the data platform for SenseTime AI research. We propose and deploy deep learning-based algorithms to accelerate manual data annotations (i.e. image segmentation, image matting, 3D object detection).
I participate in OpenPointCloud, an open-source algorithm library of deep learning-based point cloud compression & processing, mainly contrbute in the manifold learning-based point cloud representation learning method.


Academic Activities