74 citations · 174 across the 9 of their papers we have counts for
9 papers
MMATR-X: High-Resolution Optical-SAR Joint Pretraining via Conditioned Alignment and Degraded-Target Prediction
Bowen Peng, Yongxiang Liu, Jie Zhou +4
Joint optical and Synthetic Aperture Radar (SAR) pretraining seeks cross-modal gains over representations learned from either modality alone. This is challenging because the modali…
HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation
Yongxiang Liu, Jie Zhou, Yafei Song +2
Synthetic Aperture Radar (SAR) imagery generation is essential for deepening the study of scattering mechanisms, establishing trustworthy electromagnetic scene models, and fundamen…
A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation
Shuzhou Sun, Li Liu, Tianpeng Liu +4
Existing two-stage Scene Graph Generation (SGG) frameworks typically incorporate a detector to extract relationship features and a classifier to categorize these relationships; the…
SAR-GTR: Attributed Scattering Information Guided SAR Graph Transformer Recognition Algorithm
Xuying Xiong, Xinyu Zhang, Weidong Jiang +3
Utilizing electromagnetic scattering information for SAR data interpretation is currently a prominent research focus in the SAR interpretation domain. Graph Neural Networks (GNNs)…
Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning
Huali Xu, Li Liu, Tianpeng Liu +3
Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain training strategies to enhance model transferability, face challenges with large-scale pre-trai…
DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images
Zhou Jie, Xiao Chao, Peng Bo +4
Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution-…
LDSF: Lightweight Dual-Stream Framework for SAR Target Recognition by Coupling Local Electromagnetic Scattering Features and Global Visual Features
Xuying Xiong, Xinyu Zhang, Weidong Jiang +1
Mainstream DNN-based SAR-ATR methods still face issues such as easy overfitting of a few training data, high computational overhead, and poor interpretability of the black-box mode…
Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
Weijie Li, Yang Wei, Tianpeng Liu +5
The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automati…
Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection
Junjing Zheng, Xinyu Zhang, Yongxiang Liu +3
In the field of unsupervised feature selection, sparse principal component analysis (SPCA) methods have attracted more and more attention recently. Compared to spectral-based metho…