Eu Wern Teh

Research Assistant
Machine Learning Research Group
School of Engineering
University of Guelph
CV • Google Scholar

About Me

I am an AI Research Scientist working with LG Toronto AI Lab. My current research focus is on Speech-Driven Animation. I completed my Doctorate at the University of Guelph where I am advised by Prof. Graham Taylor. I received both of my M.Sc. and B.Sc degrees in Computer Science from the University of Manitoba. My research is focused on annotation-efficient learning, a.k.a learning with less label, where I explore ways to train AI models in a SCarcely Annotated Data Environment (SCADE), tackling Cancer Classification in Digital Pathology, Fine-Grained Bird Species analysis and other Commercial products. Currently, I am expanding my expertise in Speech-Driven Animation, where 3D-meshes are a scarce commodity.

Recent News

^NEW 02/2024: My paper about 3D Face Tracking from 2D Videos is accepted to CVPR2024!
09/2022: I started my role as an AI Research Scientist with LG Toronto AI Lab
04/2022: My paper about understanding image and input resolution for Digital Pathology is accepted to CRV 2022!
04/2022: My paper about semi-supervised semantic segmentation is accepted to CRV 2022!
01/2022: My paper about annotation-efficient learning via Scribble Supervision is accepted to ISBI 2022!
07/2020: My paper about metric learning is accepted to ECCV 2020!
05/2020: I have started my research internship with Modiface.
01/2020: My paper about annotation-efficient learning is accepted to ISBI 2020!
05/2019: I have a paper accepted to MIDL 2019!
04/2019: I have a paper accepted to CRV 2019!
07/2018: Attended ANR-NSERC Deepvision Meeting in Iceland. (Canada-France research collaboration)
07/2018: Presented at the Machine Learning workshop in Iceland
07/2018: Attended 2018 Deep Learning Summer School in Toronto
08/2017: I have a paper accepted to VCIP 2017!
07/2017: I have a paper accepted to BMVC 2017!
07/2016: I have a paper accepted to BMVC 2016!

Research

	3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow Felix Taubner, Prashant Raina, Mathieu Tuli, Eu Wern Teh, Chul Lee, Jinmiao Huang Computer Vision and Pattern Recognition (CVPR), 2024. [paper]
	Learning with less labels in Digital Pathology via Scribble Supervision from natural images Eu Wern Teh and Graham Taylor International Symposium on Biomedical Imaging (ISBI), 2022. [paper]
	Understanding the impact of image and input resolution on deep digital pathology patch classifiers Eu Wern Teh and Graham Taylor Conference on Computer and Robot Vision (CRV), 2022. [paper]
	The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation Eu Wern Teh and Terrance DeVries and Brendan Duke and Ruowei Jiang and Parham Aarabi and Graham Taylor Conference on Computer and Robot Vision (CRV), 2022. [paper]
	ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis Eu Wern Teh and Terrance DeVries and Graham Taylor European Conference on Computer Vision (ECCV), 2020. [paper] [source code]
	Learning with less data via Weakly Labeled Patch Classification in Digital Pathology Eu Wern Teh and Graham Taylor International Symposium on Biomedical Imaging (ISBI), 2020. [paper]
	Metric Learning for Patch Classification in Digital Pathology Eu Wern Teh and Graham Taylor Medical Imaging with Deep Learning (MIDL), 2019. [paper]
	Apparent Age Estimation with Relational Networks Eu Wern Teh and Graham Taylor Conference on Computer and Robot Vision (CRV), 2019. [paper] [source code]
	Object Localization in Weakly Labeled Data Using Regularized Attention Networks Eu Wern Teh, Zhenyu Guo and Yang Wang Vision Communications and Image Processing (VCIP), 2017. [paper]
	Adapting Object Detectors from Images to Weakly Labeled Videos Omit Chanda, Eu Wern Teh, Mrigank Rochan, Zhenyu Guo, and Yang Wang British Machine Vision Conference (BMVC), 2017. [paper]
	Attention Networks for Weakly Supervised Object Localization Eu Wern Teh, Mrigank Rochan, and Yang Wang British Machine Vision Conference (BMVC), 2016. [paper]
	Visual Analytics of Social Networks: Mining and Visualizing Co-authorship Networks Carson Leung, Christopher Carmichael, Eu Wern Teh Foundations of Augmented Cognition. (FAC), 2011. [paper]