Minhyun Lee

About

I am a Research Staff member at Samsung Electronics AI Center, where I work on semiconductor data intelligence and layout pattern generation. My research focuses on practical machine learning systems that remain reliable under limited supervision, noisy labels, and imperfect industrial data pipelines.

Before joining Samsung Electronics, I completed my M.S. and Ph.D. in Integrated Technology at Yonsei University, advised by Prof. Hyunjung Shim. I also interned at NAVER AI Lab with Song Park, Byeongho Heo, and Dongyoon Han. My earlier research centered on weak supervision, semantic segmentation, localization, and representation learning. More recently, I have been working on semiconductor data modeling, generative methods, and robust preprocessing for industrial AI systems.

My current interests include label-efficient learning, robust industrial vision systems, and semiconductor layout generation.

Publications

(* indicates equal contribution.)

Blind to Position, Biased in Language: Probing Mid-Layer Representational Bias in Vision-Language Encoders for Zero-Shot Language-Grounded Spatial Understanding

NM An*, I Kang*, M Lee, H Shim

arXiv preprint arXiv:2509.23098, 2025

paper

MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation

M Lee*, S Lee*, S Park, D Han, B Heo, H Shim

Transactions on Machine Learning Research (TMLR), 2025

paper code

Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation

J Choi*, S Lee*, M Lee, S Lee, H Shim

Conference on Computer Vision and Pattern Recognition (CVPR), 2025

paper code

Understanding Multi-Granularity for Open-Vocabulary Part Segmentation

J Choi*, S Lee*, S Lee, M Lee, H Shim

Neural Information Processing Systems (NeurIPS), 2024

paper code

SeiT++: Masked Token Modeling Improves Storage-efficient Training

M Lee*, S Park*, B Heo, D Han, H Shim

European Conference on Computer Vision (ECCV), 2024

paper code

Saliency as pseudo-pixel supervision for weakly and semi-supervised semantic segmentation

M Lee*, S Lee*, J Lee, H Shim

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2023

paper

Hybridmatch: Semi-supervised facial landmark detection via hybrid heatmap representations

S Kang*, M Lee*, M Kim, H Shim

IEEE Access 11, 26125-26135, 2023

paper

Threshold matters in wsss: Manipulating the activation for the robust and accurate segmentation model against thresholds

M Lee*, D Kim*, H Shim

Conference on Computer Vision and Pattern Recognition (CVPR), 2022

paper code

Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation

S Lee*, M Lee*, J Lee, H Shim

Conference on Computer Vision and Pattern Recognition (CVPR), 2021

paper code

Psynet: Self-supervised approach to object localization using point symmetric transformation

K Baek*, M Lee*, H Shim

AAAI Conference on Artificial Intelligence, 2020

paper code

Rethinking Weakly Supervised Semantic Segmentation Utilizing Both Point and Image Labels

M Lee, H Lee, H Shim

Available at SSRN 5062471, 0

paper

Patents

Weakly supervised semantic segmentation device and method based on pseudo-masks

H Shim, S Lee, M Lee

US Patent No. 11,798,171, granted October 24, 2023. JP Patent App. 2021-207591. Korean Patent App. 10-2021-0124495.

patent

Awards & Honors

2026.04 Industrial

Received the Grand Prize at the AIC Vision Awards. AI Center, Samsung Electronics
2025.10 Industrial

Selected as an AIC Proud Peer. AI Center, Samsung Electronics
2025.06 Academic

Selected as a CVPR 2025 Outstanding Reviewer. 711 / 12,593 reviewers

Experience

Samsung Electronics AI Center Dec 2024 - Present

Research Staff
Semiconductor data intelligence, layout generation, and reliable industrial machine learning systems.
Samsung Electronics SAIT Oct 2024 - Dec 2024

Research Staff
Image generation and generative modeling research.
Naver AI Lab Jul 2023 - Dec 2023

Research Intern
Mentored by Song Park, Byeongho Heo, and Dongyoon Han.
Worked on storage-efficient vision training.
Yonsei University Mar 2019 - Aug 2024

M.S. and Ph.D. in Integrated Technology
Advised by Prof. Hyunjung Shim. Research in weak supervision, semantic segmentation, and representation learning.
Yonsei University Mar 2012 - Feb 2019

B.S. in Computer Science