ZIWEN LAN Ziwen Lan

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  • Ziwen Lan

Short Biography: Ziwen Lan received the B.S. degree in Information Science from the University of Science and Technology of China, in 2020, and the M.S. degree in Information Science from Hokkaido University, Japan, in 2023. He is currently pursuing the Ph.D. degree with the Graduate School of Information Science and Technology at Hokkaido University. His research interests include multi-label classification and semantic understanding in virtual space. He is a student member of IEEE.

E-mail: lan[at]lmd.ist.hokudai.ac.jp

Biography Publication Fellowship and Awards

Biography

  • 2023/04 ~ Present Hokkaido University, Ph.D. in Information Science
  • 2021/04 ~ 2023/03 Hokkaido University, M.S. in Information Science
  • 2020/10 ~ 2021/03 Hokkaido University, Research Student
  • 2016/09 ~ 2020/06 University of Science and Technology of China, B.S in Information Science

 

Publication

Journal

  1. Chinami Fukui, Chang Wang, Ziwen Lan, Guang Li, Akihiro Tamura, Tsuyoshi Hanada, Keisuke Maeda, Sho Takahashi, Takahiro Ogawa, Miki Haseyama, “Text region detection and LMM-based digit recognition in scanned drawings,” Artificial Intelligence and Data Science, vol. 6, no. 2, pp. 173-178, 2025. 2025. [Paper]
  2. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships,” Sensors, vol. 23, no. 10, 4798, 2023. [Paper]
  3. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Hierarchical Multi-Label Attribute Classification with Graph Convolutional Networks on Anime Illustration,” IEEE Access, vol. 11, pp. 35447-35456, 2023. [Paper]

Conference

  1. Chang Wang, Chinami Fukui, Ziwen Lan, Guang Li, Akihiro Tamura, Tsuyoshi Hanada, Keisuke Maeda, Sho Takahashi, Takahiro Ogawa, Miki Haseyama, “Text detection for culvert engineering drawings based on data transformation and model fusion,” IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Kaohsiung, Taiwan, 2025. [Paper]
  2. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features,” IEEE International Conference on Image Processing (ICIP), pp. 2021-2025, Bordeaux, France, 2022. [Paper]
  3. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Multi-label Image Recognition Based on Multi-modal Graph Convolutional Networks Using Captioning Features,” IEEE Global Conference on Consumer Electronics (GCCE), pp. 273-274, Kyoto, Japan, 2021. [Paper]
  4. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Adversarial Perturbation to Prevent Concept Bleed-through in Continual Learning of Personalized Generative Models,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-5, Kyoto, 2025.
  5. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “Adversarial Attack Focused on Manipulating Semantic Information for Personalized Text-to-Image Diffusion Models,” Meeting on Image Recognition and Understanding (MIRU), pp. 1-5, Kumamoto, 2024.
  6. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “A note on multi-label image classification in animation illustration considering hierarchical relationships of attributes,” ITE Technical Report, vol. 47, no. 6, pp. 212-216, Sapporo, 2023.
  7. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “A Note on Multi-Label Image Recognition in Anime Illustration Based on Graph Convolutional Networks Using Captioning Features,” ITE Technical Report, vol. 46, no. 6, pp. 161-165, Sapporo, 2022.
  8. Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “A note on multi-label image recognition in anime illustration based on graph convolutional networks,” IEICE Technical Report, Sapporo, pp. 125-126, 2021.

 

Fellowship and Awards

  1. Hokkaido University Next Generation AI Doctoral Fellowship (2024/10 ~ 2026/03) [Link]
  2. Hokkaido University EXEX Doctoral Fellowship (2024/04 ~ 2024/09) [Link]
  3. Hokkaido University Ambitious Doctoral Fellowship (2023/04 ~ 2024/03) [Link]
  4. The 2024 IEEE Sapporo Section Encouragement Award (2025/02)
  5. IEEE GCCE 2021 Excellent Paper Award Outstanding Prize (2021/10)