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  • Kookmin University, Computer Science
  • Seoul, Korea

Organizations

@kookmin-sw @ai-kmu

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junghyeon0427/README.md

Research Interests

  • Deep Learning for Visual Perception
  • Transfer Learning
  • Autonomous Driving
  • Multi-modal Learning
  • Domain Adaptation / Generalization
  • Parameter Efficient Fine-tuning

Publications

International Conference

  • Confidence-Aware Adaptive Learning for Continual Test-Time Adaptation in Semantic Segmentation
    Junghyeon Seo, Dahuin Jung, and Jaekoo Lee
    Under Review

International Journal

  • Compatibility Review for Object Detection Enhancement through Super-Resolution (Paper Link)
    Daehee Kim, Sungmin Lee, Junghyeon Seo, Song Noh, and Jaekoo Lee
    MDPI Sensors, May 2024

Domsetic Conference

  • Domain Generalization in Semantic Segmentation using CLIP Style Prompts (Paper Link)
    Junghyeon Seo, Sungsik Kim, Seungheon Song, and Jaekoo Lee
    KCC, Jun 2024
  • MRPO: Multi-modal Read-only Prompt Optimization for Few-shot Learning (Paper Link)
    Junghyeon Seo, and Jaekoo Lee
    KICS, Jan 2024

Awards

  • 2023 Kookmin University Capstone Design Awards
    DITTO: Drone Is Tracking Twenty-four-seven Ours
    3rd Prize

  • VAIV x KIISE LLM Contest (KCC 2024)
    Fine-tuning LLM for Korean OpenQA tasks
    1st Prize

Teaching Assistant

  • Computer Programming I, 2023.03 ~ 2023.06
  • Computer Programming II, 2023.09 ~ 2023.12
  • Doosan Robotics ROKEY BOOT CAMP, 2024.07 ~ 2024.09

Reviewer Service

  • 2024 : CVPR, BMVC, ICPR

Pinned Loading

  1. AdaCoTTA AdaCoTTA Public

    Confidence-Aware Adaptive Learning for Continual Test-Time Adaptation in Semantic Segmentation

    1

  2. kookmin-sw/capstone-2023-26 kookmin-sw/capstone-2023-26 Public

    DITTO: Drone Is Tracking Twenty-four-seven Ours

    Python 7 6

  3. LLM_contest LLM_contest Public

    VAIV × KIISE AI Contest (KCC 2024)

    Jupyter Notebook

  4. CLIP-Style-Prompts-in-DG CLIP-Style-Prompts-in-DG Public

    Domain Generalization in Semantic Segmentation using CLIP Style Prompts

    Python