Junjie Fei’s Homepage
I am currently a PhD student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed Elhoseiny. Before that, I obtained my BS and MS degrees from Chongqing University and Xiamen University, respectively. I also gained valuable research experience as a visiting student / research assistant at SUSTech VIP Lab and KAUST Vision CAIR. Please refer to my CV for more details.
My recent research interests are focused on vision-language multimodal learning and artificial intelligence generated content. Feel free to drop me an email at junjiefei@outlook.com / junjie.fei@kaust.edu.sa if you are interested in collaborating.
News
- [2024/08] Join KAUST as a PhD student!
- [2023/07] 1 paper has been accepted by ICCV 2023!
- [2023/04] Project Caption Anything is publicly released!
Research
(* equal contribution)
![]() | Junjie Fei*, Mahmoud Ahmed*, Jian Ding, Eslam Mohamed Bakr, Mohamed Elhoseiny arXiv, 2024 project / paper Kestrel is a part-aware point grounding 3D MLLM, capable of comprehending and generating language and locating the position of the object and its materials at the part level. |
![]() | Junjie Fei*, Teng Wang*, Jinrui Zhang, Zhenyu He, Chengjie Wang, Feng Zheng ICCV, 2023 code / paper Improving the transferability of zero-shot captioning for out-of-domain images by addressing the modality bias and object hallucination that arise when adapting pre-trained vision-language models and large language models. |
![]() | Teng Wang*, Jinrui Zhang*, Junjie Fei*, Hao Zheng, Yunlong Tang, Zhe Li, Mingqi Gao, Shanshan Zhao arXiv, 2023 code / paper / demo Caption Anything is an interactive image‑to‑text generative tool that can generate diverse descriptions for any user-specified object within an image, providing a variety of language styles and visual controls to cater to diverse user preferences. |
![]() | Feng Han, Miao Zhong, Junjie Fei IEEE Transactions on Geoscience and Remote Sensing (2 Year IF: 8.125, ranking: 42/708) paper An efficient and accurate 3-D quantitative hybrid microwave imaging method, which incorporates 3D U-Net to further refine the reconstructed object. |
![]() | Junjie Fei, Yanjin Chen, Miao Zhong, Feng Han IEEE Transactions on Antennas and Propagation (2 Year IF: 4.824, ranking: 71/708) paper ResU-Net is proposed to directly reconstruct 3-D anisotropic objects from the received electromagnetic field data. |