Scholar
Junghun Oh
Google Scholar ID: fCFkL9EAAAAJ
Seoul National University
Citations & Impact
Citations
160
H-index
6
i10-index
6
Publications
8
Co-authors
7
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Jaeha Kim, Junghun Oh, and Kyoung Mu Lee, “Exploiting Diffusion Prior for Task-driven Image Restoration”, In International Conference on Computer Vision (ICCV), 2025.
- Junghun Oh, Sungyong Baik, and Kyoung Mu Lee, “Find A Winning Sign: Sign Is All We Need to Win the Lottery”, In International Conference on Learning Representations (ICLR), 2025.
- Cheeun Hong*, Sungyong Baik*, Junghun Oh, and Kyoung Mu Lee, “Difficulty, Diversity, and Plausibility: Dynamic Data-Free Quantization”, In Winter Conference on Applications of Computer Vision (WACV), 2025.
- Junghun Oh*, Sungyong Baik*, and Kyoung Mu Lee, “CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning”, In European Conference on Computer Vision (ECCV), 2024.
- Jaeha Kim, Junghun Oh, and Kyoung Mu Lee, “Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss”, In Computer Vision and Pattern Recognition (CVPR), 2024.
- Junghun Oh, Heewon Kim, Seungjun Nah, Cheeun Hong, Jonghyun Choi and Kyoung Mu Lee, “Attentive Fine-Grained Structured Sparsity for Image Restoration”, In Computer Vision and Pattern Recognition (CVPR), 2022.
- Junghun Oh, Heewon Kim, Sungyong Baik, Cheeun Hong, and Kyoung Mu Lee, “Batch Normalization Tells You Which Filter is Important”, Winter Conference on Applications of Computer Vision (WACV), 2022.
- Cheeun Hong*, Heewon Kim*, Sungyong Baik, Junghun Oh, and Kyoung Mu Lee, “DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks”, Winter Conference on Applications of Computer Vision (WACV), 2022.
- Sungyong Baik, Junghun Oh, Seokil Hong, and Kyoung Mu Lee, “Learning to Forget for Meta-Learning via Task-and-Layer-Wise Attenuation”, IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), accepted.
Research Experience
- Conducting research at SNU's computer vision lab, with a focus on enhancing the efficiency of deep learning, particularly in fine-tuning large models for downstream tasks, network pruning, and quantization.
Education
- Ph.D. in Computer Vision at Seoul National University (SNU), advised by Prof. Kyoung Mu Lee.
Background
- Ph.D. candidate in the Department of ECE at Seoul National University (SNU), focusing on improving efficiency in deep learning, low-rank adaptation, network pruning and quantization, continual learning, and task-driven image super-resolution.
