Sehyun Hwang

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I am a Ph.D. student at the POSTECH Computer Vision Lab, under the guidance of Prof. Suha Kwak, within the Department of Computer Science Engineering at POSTECH.

My research interest lies in various aspects of machine learning and computer vision including, but not limited to, active learning, domain adaptation, debiasing and its applications. I recently interested in using active learning to address challenges like data shortages, spurious correlations, and data imbalances.

I’m actively seeking opportunities for meaningful collaborations and internships. If you find my work interesting or have any questions, please don’t hesitate to reach out.

News

Jul 1, 2024 💼 I joined Biomedical Imaging Group at Microsoft Research Cambridge as a research intern.
May 1, 2024 💼 I joined Scalable Trustworthy AI Lab at University of Tübingen as a visiting researcher.
Mar 6, 2024 📚 A paper about weakly supervised instance segmentation with extreme points is accepted to CVPR 2024.
Mar 1, 2024 📚 A paper about actively correct segmentation labels using foundation model is accepted to ICML 2024.
Sep 22, 2023 📚 A paper about active learning for semantic segmentation is accepted to NeurIPS 2023.

Education

Sep, 2018 - Present Pohang University of Science and Technology (POSTECH), Pohang, South Korea
Integrated M.S./Ph.D. student in Computer Science and Engineering
Advisor: Prof. Suha Kwak
Mar, 2014 - Sep, 2018 Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea
B.S. in School of Undergraduate Studies

Experience

July, 2024 - Sep, 2024 Biomedical Imaging Group at Microsoft Research Cambridge, Cambridge, UK
Research Intern
  • Mentor: Maximilian Ilse
  • Participate in research projects about tube segmentation on chest X-ray.
Mar, 2024 - May, 2024 Scalable Trustworthy AI Lab at University of Tübingen, Tübingen, Germany
Visiting Researcher
  • Mentor: Prof. Seongjoon Oh
  • Participate in research projects about active learning for OOD.
Sep, 2018 - Present Computer Vision Lab at POSTECH, Pohang, South Korea
Ph.D. Student
  • Advisor: Prof. Suha Kwak
  • Participate in research projects about acive learning.

Publications

* indicates equal contribution.

  1. Active Label Correction for Semantic Segmentation with Foundation Models
    Hoyoung Kim,  Sehyun Hwang, Suha Kwak,  and Jungseul Ok
    International Conference on Machine Learning (ICML), 2024
  2. Extreme Point Supervised Instance Segmentation
    Hyeonjun Lee,  Sehyun Hwang,  and Suha Kwak
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  3. Active Learning for Semantic Segmentation with Multi-class Label Query
    Sehyun Hwang, Sohyun Lee, Hoyoung Kim, Minhyeon Oh, Jungseul Ok,  and Suha Kwak
    Conference on Neural Information Processing Systems (NeurIPS), 2023
  4. Adaptive Superpixel for Active Learning in Semantic Segmentation
    Hoyoung Kim, Minhyeon Oh,  Sehyun Hwang, Suha Kwak,  and Jungseul Ok
    International Conference on Computer Vision (ICCV), 2023
    Best Paper Award at BK21 Paper Award
  5. Combating label distribution shift for active domain adaptation
    Sehyun Hwang, Sohyun Lee, Sungyeon Kim, Jungseul Ok,  and Suha Kwak
    European Conference on Computer Vision (ECCV), 2022
    Qualcomm Innovation Fellowship Winner, Gold Prize at IPIU paper Award, Excellent Paper Award at BK21 Paper Award
  6. Learning debiased classifier with biased committee
    Nayeong Kim,  Sehyun Hwang, Sungsoo Ahn, Jaesik Park,  and Suha Kwak
    Conference on Neural Information Processing Systems (NeurIPS), 2022
    Excellent Paper Award at BK21 Paper Award
  7. Learning to Detect Semantic Boundaries with Image-Level Class Labels
    Namyup Kim*,  Sehyun Hwang*,  and Suha Kwak (*equal contribution)
    International Journal of Computer Vision (IJCV), 2022
    Honorable Mention @ Samsung HumanTech Paper Award

Honors and Awards

BK21 Best Paper Award, POSTECH CSE (2023)
  • Best Paper Award - Adaptive Superpixel for Active Learning in Semantic Segmentation
POSTECHIAN Fellowship Award (2023)
  • Winner ($5,000)
BK21 Best Paper Award, POSTECH CSE (2023)
  • Excellence Award - Learning debiased classifier with biased committee
  • Excellence Award - Combating label distribution shift for active domain adaptation
Qualcomm Innovation Fellowship South Korea (2022)
  • Winner ($3,000) - Combating label distribution shift for active domain adaptation
IPIU Best Paper Award, IPIU, 2022
  • Gold Prize - Combating label distribution shift for active domain adaptation
The 26th HumanTech Paper Award, Samsung Electronics Co., Ltd. (2020)
  • The Honorable Mention ($3,000) - Learning to Detect Semantic Boundaries with Image‑Level Class Labels

Professional Services

Reviewer
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • International Conference on Learning Representations (ICLR): 2025
  • European Conference on Computer Vision (ECCV): 2024
  • International Conference on Machine Learning (ICML): 2024
  • Conference on Computer Vision and Pattern Recognition (CVPR): 2023-2024
  • Asian Conference on Computer Vision (ACCV 2022,) 2024
  • Conference on Neural Information Processing Systems (NeurIPS): 2022-2023
  • International Conference on Computer Vision (ICCV): 2023
  • Winter Conference on Applications of Computer Vision (WACV): 2022