Gang Li

I am currently a Ph.D. candidate at the Institute of Software, Chinese Academy of Sciences (CAS), working on Self-supervised Learning and Diffusion Models. In June 2025, I will obtain my Ph.D. degree from Institute of Software, Chinese Academy of Sciences. If you have any good job opportunities (including postdoctoral research positions, algorithm researchers, algorithm engineers, and other related roles), please feel free to contact me at ucasligang[at]gmail[dot]com.

My current research interests center on AIGC (Artificial Intelligence Generated Content). Specific research interests include, but are not limited to:

Controllable generative diffusion models.

Efficient training and sampling of generative diffusion models.

Before this, I focused on the directions of Masking Image Modeling (MIM) and Vision Transformers (ViTs). By the way, we have compiled a repository on GitHub featuring a highly popular (300+ stars) paper list on Masked Image Modeling (MIM). The link is available at awesome-MIM.

Curriculum Vitae

Selected Papers [Full paper list at Google Scholar]

Preprint

• FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models [pdf] [Project page]
  Feihong He, Gang Li, Mengyuan Zhang, Leilei Yan, Lingyu Si, Fanzhang Li.
  arXiv:2401.15636.

• Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models. [pdf] [Project page] [Code]
  Feihong He, Gang Li, Lingyu Si, Leilei Yan, Shimeng Hou, Hongwei Dong, Fanzhang Li.
  arXiv:2309.08251.

• 3ddesigner: Towards photorealistic 3d object generation and editing with text-guided diffusion models. [pdf] [Project page]
  Gang Li, Heliang Zheng, Chaoyue Wang, Chang Li, Changwen Zheng, Dacheng Tao.
  arXiv:2211.14108.

Publication

• Semmae: Semantic-guided masking for learning masked autoencoders. [pdf] [code]
  Gang Li, Heliang Zheng, Daqing Liu, Chaoyue Wang, Bing Su, Changwen Zheng.
  Neural Information Processing Systems (NeurIPS), 2022. CCF-A .

• Simvit: Exploring a simple vision transformer with sliding windows [pdf] [code]
  Gang Li, Di Xu, Xing Cheng, Lingyu Si, Changwen Zheng.
  IEEE International Conference on Multimedia and Expo (ICME), 2022. CCF-B Oral.

• Transductive distribution calibration for few-shot learning [pdf]
  Gang Li, Changwen Zheng, Bing Su.
  Neurocomputing (**), 2022. CCF-C、SCI .

Honors and Awards

• Merit student of University of Chinese Academy of Sciences (UCAS), 2023.
• Outstanding member of the Communist Youth League of the University of Chinese Academy of Sciences (UCAS), 2020.
• National Encouragement Scholarship, 2018.
• National Scholarship for undergraduate students, 2017.