GUANG LI Guang Li

Short Bio: Guang Li is a Faculty Member of the Laboratory of Media Dynamics (LMD) and serves as a Specially Appointed Assistant Professor at the Education and Research Center for Mathematical and Data Science (MDS), Hokkaido University. He earned his B.S. degree in Software Engineering and B.A. dual degree in Japanese from Dalian University of Technology, China, in 2019, and his M.S. and Ph.D. degrees in Information Science from Hokkaido University, Japan, in 2022 and 2023, advised by Prof. Miki Haseyama and Prof. Takahiro Ogawa.

His research has been featured in renowned media, including Scientific American, Nikkei, NHK, Synced, AI Era, Deccan Herald, and Analytics India Magazine. He received the 2024 IEEE Sapporo YP Best Researcher Award for contributions to Dataset Distillation. He co-organized the 1st CVPR Workshop on Dataset Distillation and The First Dataset Distillation Challenge at ECCV. He has regularly served as an Area Chair for major international conferences, such as ACM MM and MICCAI, and as a Program Committee Member for top-tier conferences, including NeurIPS, ICLR, ICML, CVPR, ICCV, and ECCV. He also serves as a reviewer for prestigious journals, including TPAMI, TIP, TMM, TMI, and MedIA.

I’m open to collaborating on dataset distillation and other related topics. Feel free to reach out!
E-mail: guang {at} lmd.ist.hokudai.ac.jp

        

News

[2025/09] One paper was accepted to NeurIPS 2025!

[2025/08] Invited to serve as a reviewer for IEEE TPAMI!

[2025/07] Our paper has been selected as ICCV 2025 Highlight (Top 2%)!

[2025/06] One paper was accepted to ICCV 2025!

[2025/05] DD-Ranking was released!

[2025/04] Obtained funding of JSPS Grant-in-Aid for Early-Career Scientists!

[2025/03] One paper was presented at AAAI 2025!

[2025/02] Invited to serve as an Area Chair for ACM MM 2025!

[2025/01] Invited to serve as an Area Chair for MICCAI 2025!

Biography

  • 2023/10 ~ Present Hokkaido University, Assistant Professor
  • 2022/04 ~ 2023/09 Hokkaido University, Ph.D. in Information Science
  • 2020/04 ~ 2022/03 Hokkaido University, M.S. in Information Science
  • 2015/09 ~ 2019/06 Dalian University of Technology, B.A. in Japanese
  • 2015/09 ~ 2019/06 Dalian University of Technology, B.S. in Software Engineering

Highlights

Publication

Preprint

  • Minghao Yang, Ren Togo, Guang Li, Takahiro Ogawa, Miki Haseyama, “Adaptive shared experts with LoRA-based mixture of experts for multi-task learning,” 2025. [arXiv]
  • Zekai Li, Xinhao Zhong, Samir Khaki, Zhiyuan Liang, Yuhao Zhou, Mingjia Shi, Ziqiao Wang, Xuanlei Zhao, Wangbo Zhao, Ziheng Qin, Mengxuan Wu, Pengfei Zhou, Haonan Wang, David Junhao Zhang, Jia-Wei Liu, Shaobo Wang, Dai Liu, Linfeng Zhang, Guang Li, Kun Wang, Zheng Zhu, Zhiheng Ma, Joey Tianyi Zhou, Jiancheng Lv, Yaochu Jin, Peihao Wang, Kaipeng Zhang, Yiran Huang, Zeynep Akata, Zhiwei Deng, Xindi Wu, George Cazenavette, Yuzhang Shang, Justin Cui, Jindong Gu, Qian Zheng, Hao Ye, Shuo Wang, Xiaobo Wang, Yan Yan, Angela Yao, Mike Zheng Shou, Tianlong Chen, Hakan Bilen, Baharan Mirzasoleiman, Manolis Kellis, Konstantinos N. Plataniotis, Zhangyang Wang, Bo Zhao, Yang You, Kai Wang, “DD-Ranking: Rethinking the evaluation of dataset distillation,” 2025. [arXiv] [Code] [Link]

Conference

  • [NeurIPS 2025] Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Hyperbolic dataset distillation,” International Conference on Neural Information Processing Systems (NeurIPS), San Diego, CA, USA, 2025. (Acceptance Rate: 24.5%) [arXiv] [Paper] [Code] [Link]
  • [NeurIPSW 2025] Linfeng Ye, Shayan Mohajer Hamidi, Guang Li, Takahiro Ogawa, Miki Haseyama, Konstantinos N. Plataniotis, “Information-guided diffusion sampling for dataset distillation,” International Conference on Neural Information Processing Systems (NeurIPS) Workshops, San Diego, CA, USA, 2025. [arXiv] [Link]
  • [ICCV 2025 Highlight] Yawen Zou, Guang Li, Duo Su, Zi Wang, Jun Yu, Chao Zhang, “Dataset distillation via vision-language category prototype,” IEEE/CVF International Conference on Computer Vision (ICCV), Honolulu, HI, USA, 2025. (Highlight Rate: 2.3%, Acceptance Rate: 24.0%) [arXiv] [Paper] [Code] [Link]
  • [ICCVW 2025] Mingzhuo Li, Guang Li, Jiafeng Mao, Linfeng Ye, Takahiro Ogawa, Miki Haseyama, “Task-specific generative dataset distillation with difficulty-guided sampling,” IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Honolulu, HI, USA, 2025. [arXiv] [Paper] [Code] [Link]
  • [ICIP 2025] Mingzhuo Li, Guang Li, Jiafeng Mao, Takahiro Ogawa, Miki Haseyama, “Diversity-driven generative dataset distillation based on diffusion model with self-adaptive memory,” IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, pp. 415-420, 2025. [arXiv] [Paper] [Code]
  • [ICASSP 2025] Ren Tasai, Guang Li, Ren Togo, Minghui Tang, Takaaki Yoshimura, Hiroyuki Sugimori, Kenji Hirata, Takahiro Ogawa, Kohsuke Kudo, Miki Haseyama, “Continual self-supervised learning considering medical domain knowledge in chest CT images,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hyderabad, India, pp. 1-5, 2025. [arXiv] [Paper]
  • [ICASSP 2025] Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Generative dataset distillation based on self-knowledge distillation,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hyderabad, India, pp. 1-5, 2025. [arXiv] [Paper]
  • [AAAI 2025] Wenbo Huang, Jinghui Zhang, Guang Li, Lei Zhang, Shuoyuan Wang, Fang Dong, Jiahui Jin, Takahiro Ogawa, Miki Haseyama, “Manta: Enhancing mamba for few-shot action recognition of long sub-sequence,” AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, PA, USA, pp. 3751-3759, 2025. (Acceptance Rate: 23.4%) [arXiv] [Paper]
  • [ECCVW 2024] Duo Su, Junjie Hou, Guang Li*, Ren Togo, Rui Song, Takahiro Ogawa, Miki Haseyama, “Generative dataset distillation based on diffusion model,” European Conference on Computer Vision (ECCV) Workshops, Milan, Italy, pp. 83-94, 2024. (The 3rd place in The First Dataset Distillation Challenge, Team Lead) [arXiv] [Paper] [Code]
  • [ECCVW 2024] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “RGMIM: Region-guided masked image modeling for learning meaningful representations from X-ray images,” European Conference on Computer Vision (ECCV) Workshops, Milan, Italy, pp. 148-157, 2024. [arXiv] [Paper] [Link]
  • [ACM MM 2024] Zhen Wang, Dongyuan Li, Guang Li, Ziqing Zhang, Renhe Jiang, “Multimodal low-light image enhancement with depth information,” ACM International Conference on Multimedia (ACM MM), Melbourne, Australia, pp. 4976-4985, 2024. (Acceptance Rate: 26.2%) [Paper] [Dataset]
  • [ICIP 2024] Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Cross-domain few-shot in-context learning for enhancing traffic sign recognition,” IEEE International Conference on Image Processing (ICIP), pp. 2564-2570, Abu Dhabi, UAE, 2024. [arXiv] [Paper]
  • [CVPRW 2024] Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Generative dataset distillation: Balancing global structure and local details,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 7664-7671, Seattle, WA, USA, 2024. [arXiv] [Paper]
  • [AAAIW 2023] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation for medical dataset sharing,” AAAI Conference on Artificial Intelligence (AAAI) Workshops, pp. 1-6, Washington, DC, USA, 2023. [arXiv] [Link]
  • [ICASSP 2022] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-knowledge distillation based self-supervised learning for COVID-19 detection from chest X-ray images,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1371-1375, Singapore, 2022. [arXiv] [Paper]
  • [ICASSP 2022] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “TriBYOL: Triplet BYOL for self-supervised representation learning,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3458-3462, Singapore, 2022. (Top 10 Self-supervised Learning Models in 2022) [arXiv] [Paper]
  • [ICIP 2020] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Soft-label anonymous gastric X-ray image distillation,” IEEE International Conference on Image Processing (ICIP), pp. 305-309, Abu Dhabi, UAE, 2020. (The First Paper to Explore Real-world Dataset Distillation) [arXiv] [Paper]
  • [GCCE 2020 Best Student Paper] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Complexity evaluation of medical image data for classification problem based on spectral clustering,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 667-669, Kobe, Japan, 2020. [Paper]
  • ＋11 International Conference Papers
  • ＋18 Domestic Conference Papers

Journal

  • [CIBM 2025] Ayaka Tsutsumi, Guang Li, Ren Togo, Takahiro Ogawa, Satoshi Kondo, Miki Haseyama, “Dual-model weight selection and self-knowledge distillation for medical image classification,” 2025. [arXiv] [Paper]
  • [RS 2025] Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “GeoJapan Fusion Framework: A large multimodal model for regional remote sensing recognition,” Remote Sensing, vol. 17, no. 17, 3044, 2025. [Paper]
  • [KBS 2025] Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Cross-domain multi-step thinking: Zero-shot fine-grained traffic sign recognition in the wild,” Knowledge-Based Systems, vol. 327, 114172, 2025. [arXiv] [Paper]
  • [FOAR 2025] Xin Gao, Hirofumi Ueda, Meng Qu, Guang Li, Xiaojin Li, Menglin Xu, “Reimagining tourism pedestrian street aesthetics through machine learning: Understanding the role of spatial form based on a case study in Luoyang, China,” Frontiers of Architectural Research, 2025. [Paper]
  • [JG 2025] Guang Li, Katsuhiro Mabe, Miki Haseyama, “Clarifying diagnostic scope and future perspectives in AI-assisted diagnosis of Helicobacter pylori infection,” Journal of Gastroenterology, vol. 60, no. 9, 1209-1210, 2025. [Paper]
  • [KBS 2025] Zhen Wang, Yaozhu Wu, Dongyuan Li, Guang Li, Peide Zhu, Ziqing Zhang, Renhe Jiang, “LiDAR-assisted image restoration for extreme low-light conditions,” Knowledge-Based Systems, vol. 316, 113382, 2025. [Paper]
  • [JG 2025] Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Fumiyuki Shiratani, Masashi Hirota, Takahiro Ogawa, Miki Haseyama, “Multi-stage deep learning for classification of Helicobacter pylori infection status using endoscopic images,” Journal of Gastroenterology, vol. 60, no. 4, pp. 408-415, 2025. [Paper]
  • [JSCE 2024] Guang Li, Ren Togo, Keisuke Maeda, Akinori Sako, Isao Yamauchi, Tetsuya Hayakawa, Shigeyuki Nakamae, Takahiro Ogawa, Miki Haseyama, “Automatic recognition of algal bed areas based on a large-scale semantic segmentation model for estimating CO2 absorption by blue carbon,” Journal of JSCE, vol. 80, no. 17, 24-17286, 2024. (The Ports and Harbours Association of Japan Best Paper Award) [Paper]
  • [RS 2024] Guang Li, Ren Togo, Keisuke Maeda, Akinori Sako, Isao Yamauchi, Tetsuya Hayakawa, Shigeyuki Nakamae, Takahiro Ogawa, Miki Haseyama, “Algal bed region segmentation based on a ViT adapter using aerial images for estimating CO2 absorption capacity,” Remote Sensing, vol. 16, no. 10, 1742, 2024. (The Research was Featured in Nikkei) [Paper]
  • [NN 2024] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Importance-aware adaptive dataset distillation,” Neural Networks, vol. 172, 106154, 2024. [arXiv] [Paper]
  • [IEICE 2024] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation using parameter pruning,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E107-A, no. 6, pp. 936-940, 2024. [arXiv] [Paper]
  • [IJCARS 2023] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised learning for gastritis detection with gastric X-ray images,” International Journal of Computer Assisted Radiology and Surgery, vol. 18, no. 10, pp. 1841-1848, 2023. [arXiv] [Paper]
  • [CIBM 2023] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling,” Computers in Biology and Medicine, vol. 158, 106877, 2023. [arXiv] [Paper]
  • [IJCARS 2023] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on self-supervised transfer learning using chest X-ray images,” International Journal of Computer Assisted Radiology and Surgery, vol. 18, no. 4, pp. 715-722, 2023. [arXiv] [Paper]
  • [CMPB 2022] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Compressed gastric image generation based on soft-label dataset distillation for medical data sharing,” Computer Methods and Programs in Biomedicine, vol. 227, 107189, 2022. [arXiv] [Paper]
  • [MTAP 2022] Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset complexity assessment based on cumulative maximum scaled area under Laplacian spectrum,” Multimedia Tools and Applications, vol. 81, no. 22, pp. 32287-32303, 2022. [arXiv] [Paper]
  • ＋4 Journal Papers

Competition

Invited Talk

  • Hokkaido University, The 4th Hokkaido Young Professionals Workshop, “The Past, Present, and Future of Dataset Distillation,” 2024/12. (Invited by Prof. Takanori Sato)

Award

First Author

  • The Ports and Harbours Association of Japan Best Paper Award (2025/05)
  • ECCV 2024 The First Dataset Distillation Challenge Track 2 Third Place (Team Lead, 2024/09)
  • The 2024 IEEE Sapporo YP Best Researcher Award (2024/07)
  • The 2023 IEEE Sapporo Section Encouragement Award (2024/02)
  • The Dean’s Award (Ph.D. Degree, Early Graduation, 2023/09)
  • The First Hokkaido University Medical AI Symposium Outstanding Research Award (2022/11)
  • IEICE Hokkaido Section Student Encouragement Award (M.S. Degree, 2022/03)
  • IEEE GCCE 2020 Best Student Paper Award (2020/10)
  • Outstanding Graduate of Dalian University of Technology (B.S. and B.A. Degrees, 2019/06)

Co-Author

  • IEEE GCCE 2025 Excellent Student Poster Award Outstanding Prize (Shuailin Du, 2025/09)
  • 令和6年度電気・情報関係学会北海道支部連合大会 若手優秀論文発表賞 (Ren Tasai, 2024/11)
  • IEEE GCCE 2024 Presentation Award (Ayaka Tsutsumi, 2024/11)

Funding

  • JSPS Grant-in-Aid for Early-Career Scientists (2025/04/01 ~ 2028/03/31) [Link]
  • JSPS Grant-in-Aid for Research Activity Start-up (2024/07/31 ~ 2026/03/31) [Link]
  • Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research (2022/04/01 ~ 2023/09/30) [Link]

Media Coverage

  • “CO2 60トン吸収認証 えりものブルーカーボン 雑海藻駆除しコンブ増,” 北海道新聞, 2024/01/10. [Link]
  • “北海道えりも町、ブルーカーボンでクレジット取得,” 日本経済新聞, 2024/01/05. [Link]
  • “CO2吸収量、森林の9倍 えりものコンブ 町の調査報告,” 北海道新聞, 2023/09/27. [Link]
  • “コンブで脱炭素推進 開発局がCO2吸収量初調査 漁業者の収入増も狙う,” 北海道新聞, 2023/07/01. [Link]
  • “えりも沿岸域でブルーカーボン 町などが検討会,” 北海道建設新聞, 2023/06/09. [Link]
  • “コンブのCO2吸収量、年内にも算出 えりも町など検討会発足,” 北海道新聞, 2023/06/07. [Link]
  • “えりも町 海中で二酸化炭素を吸収「ブルーカーボン」の検討会,” NHK NEWS WEB, 2023/06/07. [Link]
  • “北海道開発局、えりも町で「ブルーカーボン」調査,” 日本経済新聞, 2023/04/26. [Link]
  • “2022 Top10自监督学习模型发布! 美中两国8项成果霸榜,” 新智元, 2022/11/12. [Link]
  • “Top 10 Self-supervised Learning Models in 2022,” Analytics India Magazine, 2022/11/02. [Link]
  • “一个项目帮你了解数据集蒸馏Dataset Distillation,” 机器之心, 2022/10/09. [Link]
  • “Most Popular AI Research Aug 2022,” LibHunt, 2022/09/03. [Link]
  • “AIでインフラ点検 北海道大情報科学研究院,” 日本経済新聞, 2021/06/16. [Link]
  • “How to Make Artificial Intelligence More Democratic,” Scientific American, 2021/01/02. [Link]

Academic Service

Organizer

  • The First Dataset Distillation Challenge in conjunction with ECCV 2024
  • 1st CVPR Workshop on Dataset Distillation in conjunction with CVPR 2024

Area Chair

  • ACM International Conference on Multimedia (ACM MM) 2024, 2025
  • International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2025
  • International Conference on Medical Imaging with Deep Learning (MIDL) 2025, 2026
  • IEEE International Symposium on Biomedical Imaging (ISBI) 2025
  • International Joint Conference on Neural Networks (IJCNN) 2025

Program Committee Member

  • Conference on Neural Information Processing Systems (NeurIPS)
  • International Conference on Learning Representations (ICLR)
  • International Conference on Machine Learning (ICML)
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE/CVF International Conference on Computer Vision (ICCV)
  • European Conference on Computer Vision (ECCV)
  • + 13 Conferences

Journal Reviewer

  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE Transactions on Image Processing (TIP)
  • IEEE Transactions on Multimedia (TMM)
  • IEEE Transactions on Medical Imaging (TMI)
  • Medical Image Analysis (MedIA)
  • + 27 Journals

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