Loading [MathJax]/extensions/MathZoom.js
Yunho Jeon - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Yunho Jeon

Publications
5
Citations
117
Publications by Year
20172025
Co-Authors:
Show All Co-Authors (9)
Image of author Yunho Jeon

Yunho Jeon

Affiliation

Department of Artificial Intelligence Software
Hanbat National University
Sejong, Republic of Korea

Publication Topics

Biography

Yunho Jeon received the B.S. degree in mathematics and the M.S. degree in computer science from Seoul National University, in 2006 and 2008, respectively, and the Ph.D. degree in electrical engineering from Korea Advanced Institute of Science and Technology (KAIST), in 2019. From 2008 to 2019, he was with the Agency for Defense Development, South Korea, as a Senior Researcher. After receiving his Ph.D., he was with SK Telecom, in 2019, and mofl Inc., from 2019 to 2022. He has been an Assistant Professor with the Department of Artificial Intelligence Software, Hanbat National University, South Korea, since 2023. His research interests include image processing, computer vision, time series analysis, and deep learning.(Based on document published on 11 March 2025).
Publications
5
Citations
117
Publications by Year
20172025
Co-Authors:
Show All Co-Authors (9)
Author's Published Works

Showing 1-5 of 5 results

  • Sort

Filter Results

Show

Sort By

Results

Token-Wise Asymmetric Contrastive Learning in Countering Unknown Attacks for Face Anti-Spoofing

Jimin Min;Yunho Jeon;Yonghyun Jeong;Youngjoon Yoo;Haneol Jang

IEEE Access
Year: 2025 | Volume: 13 | Journal Article |
Face anti-spoofing (FAS) techniques are crucial for safeguarding face recognition systems against an ever-evolving landscape of spoofing attacks. While existing methods have made strides in detecting known attacks, robustness against unknown, potentially more sophisticated attack types remains a critical and unresolved challenge in the field. In this paper, we propose a novel framework integrating...Show More

Token-Wise Asymmetric Contrastive Learning in Countering Unknown Attacks for Face Anti-Spoofing

Jimin Min;Yunho Jeon;Yonghyun Jeong;Youngjoon Yoo;Haneol Jang

Year: 2025 | Volume: 13 | Journal Article |

Tailored Channel Pruning: Achieve Targeted Model Complexity Through Adaptive Sparsity Regularization

Suwoong Lee;Yunho Jeon;Seungjae Lee;Junmo Kim

IEEE Access
Year: 2025 | Volume: 13 | Journal Article |
In deep learning, the size and complexity of neural networks have been rapidly increased to achieve higher performance. However, this poses a challenge when utilized in resource-limited environments, such as mobile devices, particularly when trying to preserve the network’s performance. To address this problem, structured pruning has been widely studied as it effectively reduces the network with l...Show More

Tailored Channel Pruning: Achieve Targeted Model Complexity Through Adaptive Sparsity Regularization

Suwoong Lee;Yunho Jeon;Seungjae Lee;Junmo Kim

Year: 2025 | Volume: 13 | Journal Article |

RealPixVSR: Pixel-Level Visual Representation Informed Super-Resolution of Real-World Videos

Tony Nokap Park;Yunho Jeon;Taeyoung Na

2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2024 | Conference Paper |
Recently, there have been significant advances in video super-resolution (VSR) techniques under blind and practical degradation settings. These techniques restore the fine details of each video frame while maintaining the temporal consistency between frames for a smooth motion. Unfortunately, many attempts still fall short in the case of real-world videos. When diverse and complex in-the-wild degr...Show More

RealPixVSR: Pixel-Level Visual Representation Informed Super-Resolution of Real-World Videos

Tony Nokap Park;Yunho Jeon;Taeyoung Na

Year: 2024 | Conference Paper |

Integrating Multiple Receptive Fields Through Grouped Active Convolution

Yunho Jeon;Junmo Kim

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2021 | Volume: 43, Issue: 11 | Journal Article |
Cited by: Papers (1)
Convolutional networks have achieved great success in various vision tasks. This is mainly due to a considerable amount of research on network structure. In this study, instead of focusing on architectures, we focused on the convolution unit itself. The existing convolution unit has a fixed shape and is limited to observing restricted receptive fields. In earlier work, we proposed the active convo...Show More

Integrating Multiple Receptive Fields Through Grouped Active Convolution

Yunho Jeon;Junmo Kim

Year: 2021 | Volume: 43, Issue: 11 | Journal Article |

Active Convolution: Learning the Shape of Convolution for Image Classification

Yunho Jeon;Junmo Kim

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017 | Conference Paper |
In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and residual networks. The convolution layer is the core of the CNN, but few studies have addressed the convo...Show More

Active Convolution: Learning the Shape of Convolution for Image Classification

Yunho Jeon;Junmo Kim

Year: 2017 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.