GUANG LI Guang Li

Short Biography: Guang Li is a Faculty Member of the Laboratory of Media Dynamics (LMD), a Specially Appointed Assistant Professor of the Education and Research Center for Mathematical and Data Science (MDSC), and a Visiting Researcher of the Data-Driven Interdisciplinary Research Emergence Department (D-RED), at 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 both in Information Science from Hokkaido University, Japan, in 2022 and 2023, respectively. His research interests include Dataset Distillation, Self-Supervised Learning, Data-Centric AI, and Medical Image Analysis.

His research has been featured in renowned media, including Scientific American, Nikkei, NHK, Synced, AI Era, Deccan Herald, and Analytics India Magazine. He curates and maintains the Awesome Dataset Distillation project, which has received over 1K stars and is well-known in the field. He co-organized the 1st Dataset Distillation Workshop at CVPR 2024 and the 1st Dataset Distillation Challenge at ECCV 2024. He has served as Area Chair for ACM MM, as well as a Program Committee Member for top-tier conferences such as NeurIPS, CVPR, ECCV, AAAI, and MICCAI. He also served as a Reviewer for prestigious journals such as TIP, TMI, MedIA, TNNLS, NN, TMM, and TCSVT. He is a member of IEEE.

I’m currently open to collaborating on dataset distillation and related topics! If you have any ideas, please feel free to drop me an email.

        

News

[2024/04] Our proposal to host the 1st Dataset Distillation Challenge was accepted to ECCV 2024! This event will be co-hosted by 19 renowned universities and research institutions from 7 countries, including MIT, Stanford, and Google! Many thanks to Prof. Yang You and Dr. Kai Wang of NUS for the invitation!

[2024/04] One paper on Dataset Distillation was accepted to CVPR 2024 Workshop! Contrats to Longzhen!

[2024/03] Awesome Dataset Distillation project now has its official Homepage! Many thanks to Longzhen!

[2024/02] Call for Papers: Please check the 1st Workshop on Dataset Distillation at CVPR 2024! I will serve as a Committee Member for this event! Many thanks to Dr. Saeed Vahidian of Duke for the invitation!

[2024/01] Invited to serve as an Area Chair for ACM MM 2024!

[2024/01] One paper on Dataset Distillation was accepted to Neural Networks!

[2024/01] Awesome Dataset Distillation project reached 1K stars!

Biography

  • 2023/10 ~ Present Hokkaido University, Specially Appointed 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

Awesome Dataset Distillation

The 1st Dataset Distillation Challenge

The 1st Dataset Distillation Workshop

Publications

Journal

  1. Guang Li, Ren Togo, Katsuhiro Mabe, Yoshihiro Tomoda, Masashi Hirota, Fumiyuki Shiratani, Shunpei Nishida, Takahiro Ogawa, Miki Haseyama, “Artificial Intelligence-assisted atrophic level classification: comparison with endoscopists and multicenter evaluation,” Endoscopy, 2024. (To submit)
  2. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiko 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, 2024. (To submit)
  3. 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, 2024. (Under review)
  4. Yuhu Feng, Jiahuan Zhang, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “A novel frame selection metric for video inpainting to enhance urban feature extraction,” Sensors, 2024. (Major revision)
  5. Guang Li, Ren Togo, Keisuke Maeda, Akinori Sako, Isao Yamauchi, Tetsuya Hayakawa, Shigeyuki Nakamae, Takahiro Ogawa, Miki Haseyama, “Algal bed region segmentation based on ViT-Adapter using aerial images for estimating CO2 absorption capacity,” Remote Sensing, 2024. (Major revision)
  6. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Importance-aware adaptive dataset distillation,” Neural Networks, vol. 172, 106154, 2024. [arXiv] [Paper]
  7. 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, 2024. [arXiv] [Paper]
  8. Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Zero-shot traffic sign recognition based on midlevel feature matching,” Sensors, vol. 23, no. 23, 9607, 2023. [Paper]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]

International Conference

  1. Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Think twice before recognizing: general fine-grained traffic sign recognition,” ACM International Conference on Multimedia (ACM MM), Melbourne, Australia, 2024. (Under review)
  2. Zhen Wang, Dongyuan Li, Guang Li, Ziqing Zhang, Renhe Jiang, “LED: Multimodal low-light image enhancement with depth information,” ACM International Conference on Multimedia (ACM MM), Melbourne, Australia, 2024. (Under review)
  3. 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), Abu Dhabi, UAE, 2024. (Under review)
  4. 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 Workshops (CVPRW), pp. 1-8, Seattle, WA, USA, 2024. [arXiv] [CVF] [Paper] [Link]
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation for medical dataset sharing,” AAAI Conference on Artificial Intelligence Workshops (AAAIW), pp. 1-6, Washington, DC, USA, 2023. [arXiv] [Link]
  6. 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] [Link]
  7. 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] [Link]
  8. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Self-supervised transfer learning for COVID-19 detection from chest X-ray images,” AAAI Conference on Artificial Intelligence Workshops (AAAIW), pp. 1-6, Vancouver, BC, Canada, 2022. [arXiv] [Link]
  9. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Triplet self-supervised learning for gastritis detection with scarce annotations,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 787-788, Kyoto, Japan, 2021. [Paper]
  10. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Cross-view self-supervised learning via momentum statistics in batch normalization,” IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1-2, Penghu, Taiwan, 2021. [Paper]
  11. 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] [Link]
  12. 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. (Best Student Paper Award) [Paper]

Domestic Conference

  1. 李 広, 藤後 廉, 前田圭介, 酒向章哲, 山内功, 早川哲也, 中前茂之, 小川 貴弘, 長谷山 美紀, “ブルーカーボンによるCO2吸収量推計へ向けた大規模セマンティックセグメンテーションモデルに基づく藻場領域の自動認識,” 第71回海岸工学講演会, p. 1, 秋田, 2024.
  2. Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Fine-grained traffic sign recognition via cross-domain few-shot in-context learning,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-5, Kumamoto, 2024.
  3. Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Generative dataset distillation considering global-local coherence,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-5, Kumamoto, 2024.
  4. 藤後 廉, 李 広, 小川 貴弘, 長谷山 美紀, “異分野連携によるデータ駆動型のAI研究に関する取組,” フォトエキサイトニクス研究拠点 第6回研究会, p. 1, 札幌, 2024.
  5. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “Dataset distillation via self-adaptive parameter matching,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-5, Hamamatsu, 2023.
  6. Yaozong Gan, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “A note on traffic sign recognition based on vision transformer adapter using visual feature matching,” ITE Technical Report, vol. 47, no. 6, pp. 208-211, Sapporo, 2023.
  7. 李 広, 藤後 廉, 小川 貴弘, 長谷山 美紀, “医療データを対象としたデータセット蒸留に関する検討,” 第1回 北海道大学医療AIシンポジウム, p. 1, 札幌, 2022. (Outstanding Research Award)
  8. Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama, “COVID-19 detection based on masked image modeling using vision transformer,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-4, Himeji, 2022.
  9. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Takahiro Ogawa, Miki Haseyama, “A note on automatic diagnosis of Helicobacter pylori infection based on self-supervised learning and self-knowledge distillation,” ITE Technical Report, vol. 46, no. 6, pp. 49-52, Sapporo, 2022.
  10. Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Hikari Shimizu, Takahiro Ogawa, Miki Haseyama, “A note on automatic diagnosis of Helicobacter pylori infection based on EfficientNet with flooding loss,” ITE Technical Report, vol. 45, no. 4, pp. 23-26, Sapporo, 2021.
  11. 李 広, 藤後 廉, 小川 貴弘, 長谷山 美紀, “nAULSに基づくデータセットの複雑性評価に関する検討,” 令和2年度 電気・情報関係学会北海道支部連合大会, pp. 98-99, 札幌, 2020.

Awards

  • The 2023 IEEE Sapporo Section Encouragement Award (2024/02, ICASSP 2022)
  • The Dean’s Award (2023/09, Ph.D. Degree)
  • The First Hokkaido University Medical AI Symposium Outstanding Research Award (2022/11)
  • IEICE Hokkaido Section Student Encouragement Award (2022/03, M.S. Degree)
  • IEEE GCCE 2020 Best Student Paper Award (2020/10)
  • Outstanding Graduate of Dalian University of Technology (2019/06, B.S. and B.A. Degrees)

Funding

  • Hokkaido University-Hitachi Joint Cooperative Support Program for Education and Research (2022/04 ~ 2023/09) [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. (AIを用いた画像処理による藻場判別) [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. (AIを用いた画像処理による藻場判別) [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. (AIを用いた画像処理による藻場判別) [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. (AIを用いた画像処理による藻場判別) [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. (AIを用いた画像処理による藻場判別) [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. (AIを用いた画像処理による藻場判別) [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. (AIを用いた画像処理による藻場判別) [Link]
  • “浓缩就是精华: 用大一统视角看待数据集蒸馏,” CVer, 2023/01/09. (Awesome Dataset Distillation) [Link]
  • “2022 Top10自监督学习模型总结,” 极市平台, 2022/12/06. (ICASSP 2022) [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. (ICASSP 2022) [Link]
  • “Top 10 Self-supervised Learning Models in 2022,” Analytics India Magazine, 2022/11/02. (ICASSP 2022) [Link]
  • “一个项目帮你了解数据集蒸馏Dataset Distillation,” 极市平台, 2022/10/09. (ICIP 2020; Awesome Dataset Distillation) [Link]
  • “一个项目帮你了解数据集蒸馏Dataset Distillation,” 机器之心, 2022/10/09. (ICIP 2020; Awesome Dataset Distillation) [Link]
  • “Most Popular AI Research Aug 2022,” LibHunt, 2022/09/03. (Awesome Dataset Distillation) [Link]
  • “メディア工学の研究動向,” 映像情報メディア年報2021-22シリーズ, 2022/01/01. (ICIP 2020) [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. (ICIP 2020) [Link]
  • “如何让人工智能技术更亲民,” 环球科学, 2021/01/08. (ICIP 2020) [Link]
  • “Top 3% Attention Score Paper,” Altmetric, 2021/01/02. (ICIP 2020) [Link]
  • “How to Make Artificial Intelligence More Democratic,” Deccan Herald, 2021/01/02. (ICIP 2020) [Link]
  • “How to Make Artificial Intelligence More Democratic,” Scientific American, 2021/01/02. (ICIP 2020) [Link]

Academic Service

Organizer

  • The 1st Workshop on Dataset Distillation at CVPR 2024
  • The 1st Dataset Distillation Challenge at ECCV 2024

Conference Area Chair

  • ACM International Conference on Multimedia (ACM MM)

Conference Program Committee Member

  • Conference on Neural Information Processing Systems (NeurIPS)
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • European Conference on Computer Vision (ECCV)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
  • IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Journal Reviewer

  • IEEE Transactions on Imaging Processing
  • IEEE Transactions on Medical Imaging
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Circuits and Systems for Video Technology
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Affective Computing
  • IEEE Transactions on Artificial Intelligence
  • Medical Image Analysis
  • Neural Networks
  • Pattern Recognition
  • Computer Vision and Image Understanding
  • Knowledge-Based Systems
  • Expert Systems with Applications
  • Information Sciences
  • Applied Soft Computing
  • Computers in Biology and Medicine
  • Artificial Intelligence in Medicine
  • Biomedical Signal Processing and Control
  • Neurocomputing
  • Digital Signal Processing
  • Optical and Quantum Electronics
  • IEICE Transactions on Information and Systems
  • ITE Transactions on Media Technology and Applications

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