Hyeongmin Lee

Integrated M.S/Ph.D Student of Computer Vision

Yonsei University

Image and Video Pattern Recognition Lab.

Biography

I am an integrated M.S/Ph.D student of Electrical & Electronic Engineering, Yonsei University. I’m interested in exploring the fundamental nature of Images & Videos and any technology for Representing & Enhancing them.
To know more about me, please check out my cv or visit my github.
Thank you :)
I am currently seeking a research scientist position in a startup in Korea. I will be graduating with a Ph.D. degree in August 2023. If you have any relevant opportunities, please don’t hesitate to reach out to me.

Interests

Computer Vision
Video Enhancement
Video Understanding
Generative Models

Education

BS in Electrical & Electronic Engineering, 2018

Yonsei University

Recent Publications

More Publications

Exploring Discontinuity for Video Frame Interpolation

Sangjin Lee*, Hyeongmin Lee*, Chajin Shin, Sangyoun Lee (*Equal Contribution)

CVPR, 2023.

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Expanded Adaptive Scaling Normalization for End to End Image Compression

Chajin Shin, Hyeongmin Lee, Hanbin Son, Sangjin Lee, Dogyoon Lee, Sangyoun Lee

ECCV, 2022.

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Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks

Hanbin Son, Taeoh Kim, Hyeongmin Lee, Sangyoun Lee

IEEE TIP, 2022.

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Regularization Strategy for Point Cloud via Rigidly Mixed Sample

Dogyoon Lee, Jaeha Lee, Junhyeop Lee, Hyeongmin Lee, Minhyeok Lee, Sungmin Woo, Sangyoun Lee

CVPR, 2021.

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Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation

Sangjin Lee, Hyeongmin Lee, Taeoh Kim, Sangyoun Lee

ICIP, 2020.

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Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition

Taeoh Kim*, Hyeongmin Lee*, MyeongAh Cho*, Hoseong Lee, Dong heon Cho, Sangyoun Lee (*Equal Contribution)

ECCVW, 2020.

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AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation

Hyeongmin Lee, Taeoh Kim, Tae-young Chung, Daehyun Pak, Yuseok Ban, Sangyoun Lee

CVPR, 2020.

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N-RPN: Hard Example Learning For Region Proposal Networks

MyeongAh Cho, Tae-young Chung, Hyeongmin Lee, Sangyoun Lee

ICIP, 2019.

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SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks

Taeoh Kim, Hyeongmin Lee, Hanbin Son, Sangyoun Lee

ICIP, 2019.

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CollaboNet: Collaboration of Generative Models by Unsupervised Classification

Hyeongmin Lee, Taeoh Kim, Eungyeol Song, Sangyoun Lee

ICIP, 2018.

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Recent Posts

Variational Inference

Jan 17, 2019 7 min read

Bayesian Learning의 핵심인 Variational Inference에 관한 포스팅입니다.

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Maximum Likelihood Estimation(MLE) & Maximum A Posterior(MAP)

Sep 4, 2018 10 min read

Maximum Likelihood Estimation과 Maximum A Posterior에 관한 포스팅입니다.

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Bayes Rule (베이즈 룰)

Last updated on Jan 4, 2021 5 min read

Bayes Rule과 기본적인 확률 Notation에 관한 포스팅입니다.

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[GAN]DiscoGAN

May 28, 2018 5 min read

Discogan 알고리즘에 관한 포스팅입니다.

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[GAN]Pix2Pix

Last updated on May 4, 2018 4 min read

Pix2Pix 알고리즘에 관한 포스팅입니다.

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Blog

Bayesian Deep Learning

Output 뿐만 아니라 output의 분포까지 파악해 내는 Bayesian Deep Learning에 관한 포스팅입니다.

Image Generation with GAN

이미지 생성에 쓰이는 기본적인 GAN 기반 알고리즘들을 간단히 알아보고 구현해보는 포스팅 모음입니다.

논문 리뷰

각종 분야의 논문들을 읽고 요약/정리해놓은 포스트입니다.

Contact

minimonia@yonsei.ac.kr
+82-2-2123-7843
Yonsei University 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Korea