Visual Correspondence and its applications. e.g., Semantic Correspondence, Representation Learning, etc. Specifically, I am interested in effective model architecture for computer vision tasks or designing efficient methods for correspondence.
Effective Learning where I focus on various methods to effectively train Deep Neural Networks such as Dropout, Semi-supervised Learning, Un-normalized Neural Networks, etc.

Also, I'm always trying to study various fields not stated above for interdisciplinary research.

• Sungkyunkwan University, Seoul, Korea

  • M.S./Ph.D. Integrated Student in Electrical and Computer Engineering
  • Mar. 2019 - Aug. 2023

• Ho Chi Minh University of Technology, Ho Chi Minh, Vietnam

  • B.S. in Computer Science
  • Aug. 2014 - Nov. 2018

• Researcher (Yonsei University DATES Lab, Seoul, Korea)
  • Mar. 2019 - Present
  • Advisor: Prof. Joon-Sung Yang

EUNNet: Efficient UN-normalized Convolution layer for stable training of Deep Residual Networks without Batch Normalization layer

Nguyen, Khanh-Binh and Choi, Jaehyuk and Yang, Joon-Sung
IEEE Access 2023
[Code] [Link]

Checkerboard Dropout: A Structured Dropout With Checkerboard Pattern for Convolutional Neural Networks

Nguyen, Khanh-Binh and Choi, Jaehyuk and Yang, Joon-Sung
IEEE Access 2022
[Code] [Link]

Boosting Semi-Supervised Learning by bridging high and low-confidence predictions

Nguyen, Khanh-Binh, and Joon-Sung Yang
Workshop on representation learning with very limited images: the potential of self-, synthetic- and formula-supervision (ICCVW), 2023.
[Code] [Link]

VSCHH 2023: A Benchmark for the View Synthesis Challenge of Human Heads

Jang, Youngkyoon, Jiali Zheng, Jifei Song, Helisa Dhamo, Eduardo Pérez-Pellitero, Thomas Tanay, Matteo Maggioni, Nguyen, Khanh-Binh et al.
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
[Link]

Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector

Nguyen, Khanh-Binh
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.
[Code] [Link]

(Oral, top 3%) SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning

Nguyen, Khanh-Binh
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.
[Code] [Link]

• 📫 How to find me:
  • 🏢 LinkedIn
  • 🔈 Github