Profile Alignment

I am a Ph.D. candidate in the the College of Electronic Science and Technology, National University of Defense Technology (Changsha, 410073, China), under the supervision of Professors Xiang Li, ‪Li Liu, and ‪Wei Yang. I received the university B.E. degree from Xi’an Jiaotong University, Xi’an, China, in 2019.

My research interests focus on computer vision, synthetic aperture radar, automatic target recognition, foundation model, and self-supervised Learning. I am actively seeking research internship and postdoctoral opportunities worldwide. For inquiries about my research or collaboration opportunities, please feel free to reach out via email at email at lwj2150508321@sina.com.

🎓 Educations

2019 – Present, Ph.D. Student, National University of Defense Technology, Changsha, China
2015 – 2019, B.E. degree, Xi’an Jiaotong University, Xi’an, China.

📚 Selected Publications

* Corresponding author

Preprint

Preprint 2025

ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild (Preprint)

Yongxiang Liu*, Weijie Li, Li Liu*, Jie Zhou, Bowen Peng, Yafei Song, Xuying Xiong, Wei Yang, Tianpeng Liu, Zhen Liu, Xiang Li*

[Paper] [BibTex] [知乎] [Code]

ATRNet-STAR dataset contains 40 distinct target types, collected with the aim of replacing the outdated though widely used MSTAR dataset and making a significant contribution to the advancement of SAR ATR research.

Journal

TIP 2025

SARATR-X: Toward Building a Foundation Model for SAR Target Recognition (TIP)

Weijie Li, Wei Yang*, Yunan Hou, Li Liu*, Yongxiang Liu*, and Xiang Li

[Paper] [BibTex] [知乎] [Code]

SARATR-X is a foundation model, which learns generalizable representations via self-supervised learning from large-scale unlabelled data and provides a corner stone for generic SAR target detection and classification.

ISPRS Journal 2024

Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture (ISPRS)

Weijie Li, Wei Yang, Tianpeng Liu, Yuenan Hou, Yuxuan Li, Zhen Liu, Yongxiang Liu*, Li Liu

[Paper] [BibTex] [知乎] [Code]

SAR-JEPA is a joint-embedding predictive architecture for SAR ATR that leverages local masked patches to predict the multi-scale SAR gradient representations of an unseen context.