Bohao Li  

Ph.D. candidate


School of Data Science
The Chinese University of Hong Kong, Shenzhen
Shenzhen, China, 518000.

Email: libohao1998@gmail.com;
Github: https://github.com/Bohao-Lee;
Google scholar: https://scholar.google.com

Biography

I am a Ph.D. candidate in the School of Data Science, The Chinese University of Hong Kong, Shenzhen , co-advised by Prof. Ruimao Zhang and Prof. Shuang Li. I got a M.E. degree in University of Chinese Academy of Sciences, Beijing in June 2023, advised by Prof. Qixiang Ye. I got a B.E. degree in Wuhan University, Wuhan in June 2020.

My research interests include computer vision and deep learning, specifically for few-shot learning and multimodal.

Publications

*Bohao Li, *Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, Ying Shan
SEED-Bench-2: Benchmarking Multimodal Large Language Models
[Paper] [Dataset] [Code] [Leaderborad]
*Bohao Li, *Rui Wang, *Guangzhi Wang, Yuying Ge, Yixiao Ge, Ying Shan
Seed-bench: Benchmarking multimodal llms with generative comprehension
[Paper] [Dataset] [Code] [Leaderborad]
Bohao Li, Chang Liu, Mengnan Shi, Xiaozhong Chen, Xiangyang Ji, Qixiang Ye
Proposal Distribution Calibration for Few-Shot Object Detection
IEEE Transactions on Neural Networks and Learning Systems, 2024
[Paper] [Code]
*Bohao Li, * Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
IEEE Conference on Computer Vision and Pattern Recognition, 2021
[Paper] [Code]
Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, Hongsheng Li
Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
IEEE Conference on Computer Vision and Pattern Recognition, 2023
[Paper] [Code]
Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, Qixiang Ye
Prototype mixture models for few-shot semantic segmentation
European Conference on Computer Vision (ECCV), 2020
[Paper] [Code]
Boyu Yang, Fang Wan, Chang Liu, Bohao Li, Xiangyang Ji, Qixiang Ye
Part-based semantic transform for few-shot semantic segmentation
IEEE Transactions on Neural Networks and Learning Systems, 2021
[Paper] [Code]

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