Wooseong Jeong

Ph.D. Candidate at KAIST, advised by Prof. Kuk-Jin Yoon at the Visual Intelligence Lab (VILAB).

prof_pic.jpg
stk14570@kaist.ac.kr

I am a Ph.D. candidate at KAIST working on efficient adaptation and merging of deep learning models. My research focuses on multi-task optimization, test-time adaptation, and LoRA/model merging to build robust AI systems that generalize across tasks, domains, and deployment scenarios.

Feel free to explore my publications, and reach out if you’d like to collaborate or have any questions.

Research Interests

  • Parameter-Efficient Learning & Model Merging — LoRA/PEFT, conflict-aware weight composition, preference-aligned merging
  • Multi-Task Learning & Scalable Optimization — task interaction, Pareto optimization, preference-based multi-objective learning
  • Test-Time Adaptation & Robust Learning under distribution shift
  • Autonomous Driving & Robotics — motion planning, vision-language-action (VLA) models, robust autonomy

Publications

2026

  1. jeong2026preference.png
    Preference-Aligned LoRA Merging: Preserving Subspace Coverage and Addressing Directional Anisotropy
    Wooseong Jeong*, Wonyoung Lee*, and Kuk-Jin Yoon
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
    arXiv Video Code Poster
  2. lee2026labelfree.png
    Label-Free Cross-Task LoRA Merging with Null-Space Compression
    Wonyoung Lee*, Wooseong Jeong*, and Kuk-Jin Yoon
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
    arXiv Video Code Poster
  3. jeong2026stabilizing.png
    Stabilizing Multi-Task Latent Spaces: Recursive Refinement with Coordinators in Partially Labeled Learning
    Wooseong Jeong*, Jegyeong Cho*, Youngho Yoon*, Jaeyoung Lee, and Kuk-Jin Yoon
    IEEE Access, 2026
    PDF

2025

  1. lee2025interaction.png
    Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
    Giwon Lee*, Wooseong Jeong*, Daehee Park, Jaewoo Jeong, and Kuk-Jin Yoon
    In The International Conference on Computer Vision (ICCV), 2025
    Highlight
    arXiv Code Poster
  2. jeong2025resolving.png
    Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning
    Wooseong Jeong and Kuk-Jin Yoon
    In The International Conference on Computer Vision (ICCV), 2025
    arXiv Code Poster
  3. jeong2025synchronizing.png
    Synchronizing Task Behavior: Aligning Multiple Tasks during Test-Time Training
    Wooseong Jeong*, Jegyeong Cho*, Youngho Yoon*, and Kuk-Jin Yoon
    In The International Conference on Computer Vision (ICCV), 2025
    arXiv Code Poster
  4. kim2025dc.png
    DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
    Jihun Kim*, Hoyong Kwon*, Hyeokjun Kweon*, Wooseong Jeong, and Kuk-Jin Yoon
    In The International Conference on Computer Vision (ICCV), 2025
    arXiv Video Code
  5. jeong2025multi.png
    Multi-View 3D Scene Abstraction From Drone-Captured RGB Images
    Wooseong Jeong*, Jihun Kim*, Hyeokjun Kweon*, and Kuk-Jin Yoon
    IEEE Access, 2025
    PDF
  6. jeong2025selective.png
    Selective Task Group Updates for Multi-Task Optimization
    Wooseong Jeong and Kuk-Jin Yoon
    In The International Conference on Learning Representations (ICLR), 2025
    arXiv Code Poster

2024

  1. jeong2024quantifying.png
    Quantifying Task Priority for Multi-Task Optimization
    Wooseong Jeong and Kuk-Jin Yoon
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
    arXiv Code Poster

2023

  1. kweon2023pixel.png
    Pixel-wise Warping for Deep Image Stitching
    Hyeokjun Kweon*, Hyeonseong Kim*, Yoonsu Kang*, Youngho Yoon*, Wooseong Jeong, and Kuk-Jin Yoon
    In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023
    arXiv