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Gilhan Park

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Hello there! I’m Gilhan Park, currently a master's student in Artificial Intelligence at Sungkyunkwan University in South Korea. I am a member of Visual Computing LABoratory advised by Prof. Jae-Pil Heo.

My research topic is centered on Computer Vision, specifically Continual Learning, Class-Incremental Segmentation, and Parameter Efficient Fine-Tuning.

If you're interested in my research or would like to engage in a scholarly discussion, please contact me via email.

Research Interests

  • Continual Learning

  • Class-Incremental Learning

  • Segmentation

  • Parameter Efficient Fine-Tuning

News

  • Nov. 2024

    'QIFK2024_69: Mitigating Background Shift in Class-Incremental Semantic Segmentation' has been selected as a finalist for Qualcomm Innovation Fellowship South Korea 2024!

  • Oct. 2024

    Attending ECCV 2024 (Milano, Italy) and presenting a poster!🔥

  • Jun. 2024

    'Mitigating Background Shift in Class-Incremental Semantic Segmentation' has been accepted for publication at ECCV, a top-tier computer vision conference!

Selected Projects

Click to see details

Mitigating Background Shift in Class-Incremental Semantic Segmentation
research

Jul. 2024

Mitigating Background Shift in Class-Incremental Semantic Segmentation

Addressing Background Shift in Class-Incremental Semantic Segmentation

Educations

Publications

Experiences

  • Jul. 2022 - Present

    Sungkyunkwan University, Suwon, South Korea

    Research Assistant, Visual Computing Lab

    Researching on the topic of Continual Learning, Class-Incremental Learning, Segmentation and Parameter Efficient Fine-Tuning

  • Oct. 2024 - Nov. 2024

    Teaching Assistant for LG Electronics Manufacturing AI Leadership Program

    Teaching Assistant

    Assisted in the LG Electronics Manufacturing AI Leadership Program by supporting project-based learning and providing guidance on AI methodologies.

  • Dec. 2021 - June. 2022

    Sungkyunkwan University, Suwon, South Korea

    Undergraduate Research Student, CSI Lab

    Conducted research on improving performance of Semantic Segmentation in remote sensing datasets.

  • July. 2021 - Dec. 2021

    Smilegate Future Lab, Pangyo, South Korea

    AI Project Contributor, SGM AI Project

    Developed real-time clothing pattern generation and style conversion under the guidance of the Smilegate AI team.

Honors & Awards

  • May. 2023 - Present

    Hyundai Motor Company Chung Mong-koo Scholarship

    Selected as a recipient of the Hyundai Motor Chung Mong-koo Foundation Scholarship

  • Nov. 2024

    Qualcomm Innovation Fellowship South Korea 2024

    Finalist

    Selected as a finalist for the Qualcomm Innovation Fellowship South Korea 2024 with the paper 'QIFK2024_69: Mitigating Background Shift in Class-Incremental Semantic Segmentation.'

  • Sep. 2022

    2nd SKKU AI-SW Education Hackathon, Gold Prize

    CodeWithMe: Extracted code images during programming lectures, utilizing a model for Document Understanding, typo correction, and code comprehension

  • Dec. 2021

    Outstanding Paper Award at ACK 2021, Korea Information Processing Society, Outstanding Paper Award

    Received Outstanding Paper Award at the ICT Conference of Korea Information Processing Society (ACK 2021) with the paper 'Edge Computing-Based Cloud Parking Management System'

  • Dec. 2021

    1st SKKU SW-AI Education Capstone Design Competition, Grand Prize

    Implemented the game 'Red Light, Green Light' from Netflix's 'Squid Game' using Jetson Nano

  • Dec. 2021

    2021 Hanium Competition, Honorable Mention

    Developed a Korean license plate recognition model using LPRnet and parking automation program

  • Oct. 2021

    1st SKKU AI-SW Education Hackathon, Silver Prize

    Focus Maker: A service to measure online learners' concentration index and provide self-feedback

  • Dec. 2021

    Completion of Smilegate AI Membership

    Participated in Smilegate AI Membership

  • Aug. 2021

    2021 AI Online Competition, 3rd Place

    Won 3rd place in the AI Online Competition hosted by the Ministry of Science and ICT with a model detecting driver negligence behavior for accident prevention

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