7 papers
MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration
Da Chang, Qiankun Shi, Lvgang Zhang +5
Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions typically either rescale updates after orthogonalization or use…
Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
Pan Yi, Weijie Li, Xiaodong Chen +3
Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR image sizes hinder deep learni…
SST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack
Yongxiang Liu, Bowen Peng, Li Liu +1
Transferable Targeted Attacks (TTAs) face significant challenges due to severe overfitting to surrogate models. Recent breakthroughs heavily rely on large-scale training data of vi…
GDROS: A Geometry-Guided Dense Registration Framework for Optical-SAR Images under Large Geometric Transformations
Zixuan Sun, Shuaifeng Zhi, Ruize Li +3
Registration of optical and synthetic aperture radar (SAR) remote sensing images serves as a critical foundation for image fusion and visual navigation tasks. This task is particul…
Enhancing Representations through Heterogeneous Self-Supervised Learning
Zhong-Yu Li, Bo-Wen Yin, Yongxiang Liu +2
Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. How…
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
Yuxuan Li, Xiang Li, Yimian Dai +5
Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing c…