5 papers
Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder
Bowen Peng, Yongxiang Liu, Jie Zhou +4
Learning robust representations across extremely heterogeneous modalities remains a fundamental challenge in multi-modal vision. As a critical and profound instantiation of this ch…
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…
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…
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)…