International Publications
2025
[J10†] Seunghyun Oh and Heewon Kim, "Accurate Baseball Player Pose Refinement Using Motion Prior Guidance," ICT Express, 2025 (accepted)
[C22] Sangmin Lee*, Sungyong Park*, and Heewon Kim, "DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI," Proc. Computer Vision and Pattern Recognition (CVPR), 2025.
[C21] Sooyoung Choi*, Sungyong Park*, and Heewon Kim, "SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses," AAAI Conference on Artificial Intelligence, 2025. [PDF] [Project Page]
[C20&J9] Chiyoung Lee, Heewon Kim, Yeri Kim, Seoyoung Kim, Kent Kwoh, Juyoung Park, Hyochol Ahn, "Predictors of the treatment effects of transcranial direct current stimulation on knee osteoarthritis pain: a machine-learning approach," International Brain Stimulation Conference, 2025 & Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, vol. 18, Issue 1, pp. 456 - 457, Feb. 2025. [PDF]
†: Undergraduate student's work
2024
[C17&J8] Chiyoung Lee, Yeri Kim, Seoyoung Kim, Beth Cohen, Kent Kwoh, Hyochol Ahn, Juyoung Park, Heewon Kim, "Trajectories of chronic pain among older Veterans: Identifying pain-worsening predictors via machine learning," Gerontological Society of America (GSA) Annual Scientific Meeting, 2024 & Innovation in Aging, vol. 8, issue supple. 1, pp. 1221, Dec. 2024. [PDF]
[J7†] Ji Hoon Bang, Eun Hee Kim, Hyung Jun Kim, Jong-Won Chung, Woo-Keun Seo, Gyeong-Moon Kim, Dong-Ho Lee, Heewon Kim*, and Oh Young Bang*, "Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs," International Journal of Molecular Sciences (IJMS), vol. 25, no. 12, pp. 1-14, Jun. 2024. [PDF]
†: Undergraduate student's work
2023
[J4] Heewon Kim and Kyoung Mu Lee, "Learning Controllable ISP for Image Enhancement," IEEE Trans. Image Processing (TIP), vol. 33, no. 1, pp. 867-880, Aug. 2023. [PDF]
[J3] Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, and Kyoung Mu Lee, "Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning," IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), vol. 46, no. 3, pp. 1441-1454, Mar. 2023. [PDF]
[C15] Heewon Kim and Kyoung Mu Lee, "NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs," International Conference on Learning Representations (ICLR), 2023. [PDF]
~ 2022
[J2] Heewon Kim, Seokil Hong, Bohyung Han, Heesoo Myeong, and Kyoung Mu Lee, "Fine-Grained Neural Architecture Search for Image Super-Resolution," Journal of Visual Communication and Image Representation (JVCI), vol. 89, no. 1, pp. 1-9, Nov. 2022. [PDF]
[J1] Chiyoung Lee and Heewon Kim, "Machine learning-based predictive modeling of depression in hypertensive populations," PLOS ONE, vol. 17, no. 7, pp. 1-17, Jul. 2022. [PDF]
[C13] Junghun Oh, Heewon Kim, Seungjun Nah, Cheeun Hong, Jonghyun Choi, and Kyoung Mu Lee, "Attentive Fine-Grained Structured Sparsity for Image Restoration," Proc. Computer Vision and Pattern Recognition (CVPR), 2022. [PDF]
[C12] 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. [PDF]
[C11] 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. [PDF]
[C10] Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaesik Min, and Kyoung Mu Lee, "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning," Proc. International Conference on Computer Vision (ICCV), 2021. (ORAL presentation) [PDF] [arXiv] [Supplementary material] [code]
[C9] Myungsub Choi, Suyoung Lee, Heewon Kim, and Kyoung Mu Lee, "Motion-Aware Dynamic Architecture for Efficient Frame Interpolation," Proc. International Conference on Computer Vision (ICCV), 2021. [PDF]
[C7] Andrey Ignatov, Andres Romero, Heewon Kim, and Radu Timofte, "Real-time video super-resolution on smartphones with deep learning, mobile ai 2021 challenge: Report," Proc. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. [PDF]
[C5] Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, and Kyoung Mu Lee, "Channel Attention Is All You Need for Video Frame Interpolation," AAAI Conference on Artificial Intelligence, 2020. [PDF]
[C4] Seungjun Nah, Sanghyun Son, Radu Timofte, Kyoung Mu Lee, Li Siyao, Ze Pan, Xiangyu Xu, Wenxiu Sun, Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, Bumjun Park, Songhyun Yu, Sangmin Kim, Jechang Jeong, Wang Shen, Wenbo Bao, Guangtao Zhai, Li Chen, Zhiyong Gaon, Guannan Chen, Yunhua Lu, Ran Duan, Tong Liu, Lijie Zhang, Woonsung Park, Munchurl Kim, George Pisha, Eyal Naor, Lior Aloni, "AIM 2019 challenge on video temporal super-resolution: Methods and results," International Conference on Computer Vision Workshop (ICCVW), 2019. [PDF]
[C3] Heewon Kim, Myungsub Choi, Bee Lim, and Kyoung Mu Lee, "Task-Aware Image Downscaling," Proc. European Conference on Computer Vision (ECCV), 2018. [PDF] [Project Page]
[C2] Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, Xintao Wang, Yapeng Tian, Ke Yu, Yulun Zhang, Shixiang Wu, Chao Dong, Liang Lin, Yu Qiao, Chen Change Loy, Woong Bae, Jaejun Yoo, Yoseb Han, Jong Chul Ye, Jae-Seok Choi, Munchurl Kim, Yuchen Fan, Jiahui Yu, Wei Han, Ding Liu, Haichao Yu, Zhangyang Wang, Honghui Shi, Xinchao Wang, Thomas S. Huang, Yunjin Chen, Kai Zhang, Wangmeng Zuo, Zhimin Tang, Linkai Luo, Shaohui Li, Min Fu, Lei Cao, Wen Heng, Giang Bui, Truc Le, Ye Duan, Dacheng Tao, Ruxin Wang, Xu Lin, Jianxin Pang, Jinchang Xu, Yu Zhao, Xinngyu Xu, Jinshan Pan, Deqing Sun, Yujin Zhang, Xibin Song, Yuchao Dai, Xueying Qin, Xuan-Phung Huynh, Tiantong Guo, Hojjat Seyed Mousavi, Tiep Huu Vu, Vishal Monga, Cristovao Cruz, Karen Egiazarian, Vladimir Katkovnik, Rakesh Mehta, Arnav Kumar Jain, Abhinav Agarwalla, Ch V Sai Praveen, Ruofan Zhou, Hongdiao Wen, Che Zhu, Zhiqiang Xia, Zhengtao Wang, and Qi Guo, "NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results," NTIRE 2017: New Trends in Image Restoration and Enhancement workshop and challenge on super-resolution in conjunction with CVPR 2017. [PDF]
[C1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution," 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on super-resolution in conjunction with CVPR 2017. (Challenge Winners, Best Paper Award of Workshop) [paper (PDF)] [code] [slide]
In computer vision, the top three conferences (CVPR, ICCV, ECCV) are considered more important and have a greater impact than most SCI journals. Oral presentations have a highly competitive acceptance rate of about 4% and poster presentations about 20%. According to the recent survey of CiteScholar, their impact factors are CVPR 5.97, ECCV 5.91, and ICCV 5.05, which correspond to the top 4% - 7% of all computer science journals and conferences. Note that CVPR is the only conference proceedings listed in the top 100 publications in Google Scholar and is ranked No.1 in Computer Science and Electrical Engineering.
IEEE TPAMI and IJCV have among the highest ISI impact factors across all computer science categories. PAMI is one of the top-ranked publications in IEEE and in all computer science journals.
컴퓨터비젼 분야에서는 CVPR, ICCV, ECCV의 3대 주요학술대회 논문 발표가 대다수의 SCI 저널 논문 발표보다 중요하게 평가됨. 이러한 3대 주요학회의 통상 논문 채택률은 23-28% 정도로 매우 경쟁이 심하고, 영향력(Impact Factor) 지수도 CVPR 5.97, ECCV 5.91, ICCV 5.05 로서 컴퓨터 분야의 모든 저널과 학술대회의 상위 4-7%에 해당됨.
특히 CVPR의 경우 Google Scholar의 영향력지수 상위 100대 출판물 중 유일한 학술대회 논문집 (83위)이며 Computer Science 및 Electrical Engineering 분야에서 가장 높은 영향력지수를 보이고 있음.
컴퓨터비젼 분야 저널의 경우, IEEE TPAMI 와 IJCV 가 top 저널들이며, PAMI는 모든 IEEE 출판물, 그리고 Electrical Engineering 및 Artificial Intelligence 분야 출판물 중 가장 높은 영향력지수를 가지고 있는 저널 중 하나임.