Cheeun Hong

Ph.D. Candidate @ Computer Vision Lab
Department of Electrical and Computer Engineering, Seoul National University

I am passionate about advancing efficient AI to optimize both model training and inference, with the ultimate goal of promoting sustainable AI. My research focuses on developing cutting-edge techniques like network quantization, pruning, and test-time adaptation, aimed at drastically reducing the computational costs while maintaining high performance. While much of my work has targeted efficiency improvements in low-level vision tasks like image restoration, my broader goal is to compress large-scale, computationally intensive models—including vision-language and generative models—to move closer to achieving on-device AI.

Keywords: Efficient AI, On-device AI

Recent News

Oct 2024 One first-co-authored paper on data-free quantization, DDPQ got accepted to WACV 2025.
Jul 2024 One first-authored paper on quantization, ODM got accepted to ECCV 2024. See you in Milano!
Feb 2024 One first-authored paper on quantization, AdaBM got accepted to CVPR 2024. See you in Seattle!

Selected Publications

  1. cover_dynadfq.png
    WACV
    Difficulty, Plausibility, and Diversity: Dynamic Data-Free Quantization
    Cheeun Hong*, Sungyong Baik*, Junghun Oh, and Kyoung Mu Lee
    In Winter Conference on Applications of Computer Vision (WACV), 2025
  2. cover_odm.png
    ECCV
    Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
    Cheeun Hong, and Kyoung Mu Lee
    In European Conference on Computer Vision (ECCV), 2024
  3. cover_adabm.png
    CVPR
    AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution
    Cheeun Hong, and Kyoung Mu Lee
    In Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  4. cover_colanet.png
    IEEE SPL
    CoLaNet: Adaptive Context and Latent Information Blending for Face Image Inpainting
    Joonkyu Park, Cheeun Hong, Sungyong Baik, and Kyoung Mu Lee
    In IEEE Signal Processing Letters (IEEE SPL), 2023
  5. cover_cadyq.png
    ECCV
    CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
    Cheeun Hong, Sungyong Baik, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee
    In European Conference on Computer Vision (ECCV), 2022
  6. cover_sls.jpg
    CVPR
    Attentive Fine-Grained Structured Sparsity for Image Restoration
    Junghun Oh, Heewon Kim, Seungjun Nah, Cheeun Hong, Jonghyun Choi, and Kyoung Mu Lee
    In Conference on Computer Vision and Pattern Recognition (CVPR), 2022
  7. cover_daq.png
    WACV
    DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
    Cheeun Hong*, Heewon Kim*, Sungyong Baik, Junghun Oh, and Kyoung Mu Lee
    In Winter Conference on Applications of Computer Vision (WACV), 2022
  8. cover_bnfi.png
    WACV
    Batch Normalization Tells You Which Filter is Important
    Junghun Oh, Heewon Kim, Sungyong Baik, Cheeun Hong, and Kyoung Mu Lee
    In Winter Conference on Applications of Computer Vision (WACV), 2022

Education

Mar 2020 Seoul National University, South Korea
Integrated Ph.D. in Electrical and Computer Engineering
Advisor: Prof. Kyoung Mu Lee

Mar 2015 ~Feb 2020 Seoul National University, South Korea
B.S. in Electrical and Computer Engineering