Jongoh Jeong

Jongoh Jeong
jeong2 [at] kaist.ac.kr / [CV] [Google Scholar] [Github] [LinkedIn]

I am a Ph.D. student in the Robotics Program at Korea Advanced Institute of Science and Technology (KAIST), South Korea, advised by Prof. Kuk-Jin Yoon at Visual Intelligence Lab. My research is aimed at robust and data-efficient visual representation learning, domain adaptation/generalization, and transfer learning for designing effective visual learning algorithms with minimal human supervision.

📌 Education

Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea

🏫 Ph.D. in Robotics (Advisor: Prof. Kuk-Jin Yoon), 2022 - Present

🎓 M.S. in Electrical Engineering (Advisor: Prof. Jong-Hwan Kim), 2020 - 2022

The Cooper Union for the Advancement of Science and Art, New York, NY, USA

🎓 B.E. in Electrical Engineering, 2014 - 2020 (*2016-2018: military duty leave)

📖 Publications
(W: Peer-reviewed Workshop, C: Peer-reviewed Conference, J: Peer-reviewed Journal, P: Pre-print, K-C: Korean Conference)
Research Interests:

Computer Vision (robust/data-efficient representation learning, domain adaptation/generalization for continual learning) [P1, P3, C5, P4]
Machine Learning (robust, safe, trustworthy, adversarial AI) [J2, C3, C4, W3, C6]
- Robotic/Machine Vision (autonomous driving, sensor fusion) [J1, C1, K-C1, C2, P2]

[P4] Multimodal Distribution Matching for Vision-Language Dataset Distillation

Jongoh Jeong*, Hoyong Kwon*, Minseok Kim*, and Kuk-Jin Yoon. (*Shared authorship)

Pre-print., Nov. 2025.

[arXiv]

[C6] Improving Black-Box Generative Attacks via Generator Semantic Consistency

Jongoh Jeong, Hunmin Yang, Jaeseok Jeong, and Kuk-Jin Yoon.
The Fourteenth International Conference on Learning Representations (ICLR), Apr. 2026. (acceptance rate 28.18%)
*Extension of [W3]

[arXiv]

[C5] DOODLE: Diffusion-based Out-of-Distribution Learning for Open-set LiDAR Semantic Segmentation

Changgyoon Oh*, Hyeonseong Kim*, Daehyun We*, Jongoh Jeong, Yujeong Chae, and Kuk-Jin Yoon. (*Shared authorship)

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Mar. 2026.

[Paper]

[W3] Boosting Adversarial Transferability with a Generative Model Perspective

Jongoh Jeong, Hunmin Yang, and Kuk-Jin Yoon
Computer Vision and Pattern Recognition (CVPR) 2025
Workshop on Generative Models for Computer Vision (GMCV 2025), Jun. 2025.

[Paper][Web]

[C4] Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks

Hunmin Yang, Jongoh Jeong, and Kuk-Jin Yoon
European Conference on Computer Vision (ECCV), Sep. 2024.
(Oral, 8.37% of accepted, 2.33% of submitted)

[Paper][Project][KAIST News]

[P3] Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Tasks

Changgyoon Oh*, Jongoh Jeong*, Jegyeong Cho*, and Kuk-Jin Yoon
Pre-print, Sep. 2024. (*Shared authorship)

[Preprint]

[C3] FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks

Hunmin Yang*, Jongoh Jeong*, and Kuk-Jin Yoon (*Shared authorship)
The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), Feb. 2024.
*Extension of [W2]

[Paper][Project]

[P2] AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding

Jongoh Jeong*, Taek-Jin Song*, Jong-Hwan Kim, and Kuk-Jin Yoon
Pre-print. (*Shared authorship)

[arXiv]

[C2] Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction

Hwan-Soo Choi*, Jongoh Jeong*, Young Hoo Cho, Kuk-Jin Yoon, and Jong-Hwan Kim. (*Shared authorship)
Robot Intelligence Technology and Applications (RiTA), Dec. 2023.

[Paper][Project]

[P1] Exploring Syn-to-Real Domain Adaptation for Military Target Detection

Jongoh Jeong, Youngjin oh, Gyeongrae Nam, Jeongeun Lee, and Kuk-Jin Yoon
Pre-print, Oct. 2023.

[arXiv]

[W2] FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks

Hunmin Yang*, Jongoh Jeong*, and Kuk-Jin Yoon (*Shared authorship)
ICCV 2023 Workshop on Adversarial Robustness In the Real World (AROW 2023), Oct. 2023.
[Paper]

[J2] Adaptive Bayesian Optimization for Fast Exploration Under Safety Constraints

Guk Han, Jongoh Jeong, and Jong-Hwan Kim.
IEEE Access. Apr. 2023.

[Paper][Project]

[K-C1] Real-time Road Obstacle Detection and Avoidance Network for Autonomous Driving under Adverse Weather

Jongoh Jeong, Taek-Jin Song, Jong-Hwan Kim, and Kuk-Jin Yoon.
IPIU. Jan. 2023.

[C1] Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather

Jongoh Jeong and Jong-Hwan Kim.
British Machine Vision Conference (BMVC), Nov. 2022.

[Paper][Project]

[J1] End-to-end Real-time Obstacle Detection Network for Safe Self-driving via Multi-task Learning

Taek-Jin Song*, Jongoh Jeong* and Jong-Hwan Kim. (*Shared authorship)
IEEE Transactions on Intelligent Transportation Systems (IEEE T-ITS), Sept. 2022.

[Paper][Project]

[W1] Using Privileged Information to Improve Prediction in Health Data: A Case Study.

Jongoh Jeong, Do Hyung Kwon, Min Joon So, Anita Raja, Shivani Ghatge, Nicolae Lari, and Ansaf Salleb Aouissi.
NeurIPS 2019 Workshop on Information Theory and Machine Learning (ITML 2019)

[Paper][Web]

🗂️ Research Projects

C-arm X-ray imaging system development for DK Medical Systems (Jul.'25 – Jun.'26)
AI/SW Project for Commissioned Education (Ministry of National Defense (MND), Jun.'22 – Dec.'26)
Synthetic Military Target Data Generation for Domain Adaptive Object Detection (LIGNex1, Oct.’22 – Sep.’23)
Development of Artificial Intelligence Technology that Continuously Improves Itself as the Situation Changes in the Real World (Korea MSIT (IITP), No.2020-0-00440, Sep.’20 –Aug.’22)
Development of Real-time Smart Solution for Solder Mount Technology (SMT) (Koh-Young Tech., Sep.’20 – Aug.’22)

📤 Academic Activities

Reviewer

AAAI, ICLR, 2026-Present
IEEE Transactions of Cybernetics (T-CYB), 2026-Present
IEEE Transactions on Geoscience and Remote Sensing (T-GRS), 2024-Present
- IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2022-Present
IEEE Access, 2022-Present
NeurIPS Workshop on Machine Learning for Health (ML4H), 2020-Present

Professional Society Membership

- IEEE Computer Society, 2025
- Order of the Engineer 2020 – Present
- IEEE-Eta Kappa Nu, Student Member 2018 – Present
- ACM, Student Member 2018 – Present

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