Eu Wern Teh

Research Assistant
Machine Learning Research Group
School of Engineering
University of Guelph
Email: euwern1987@gmail.com

CVGoogle Scholar

About Me

I am a Ph.D. candidate at the University of Guelph where I am advised by Prof. Graham Taylor. I received both of my M.Sc. and B.Sc degree 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 survive in a SCarcely Annotated Data Environment (SCADE).

Recent News

  • NEW 04/2022: My paper about understanding image and input resolution for Digital Pathology is accepted to CRV 2022!
  • NEW 04/2022: My paper about semi-supervised semantic segmentation is accepted to CRV 2022!
  • NEW 01/2022: My paper about annotation-efficient learning via Scribble Supervision is accepted to ISBI 2022!
  • NEW 10/2021: I passed my qualifying exam and am now officially a Ph.D. Candidate!
  • 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

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]

Imitation is the sincerest form of flattery!