6 papers · 1 filter
Learning Where to Focus: Density-Driven Guidance for Detecting Dense Tiny Objects
Zhicheng Zhao, Xuanang Fan, Lingma Sun +2
High-resolution remote sensing imagery increasingly contains dense clusters of tiny objects, the detection of which is extremely challenging due to severe mutual occlusion and limi…
Towards Robust Optical-SAR Object Detection under Missing Modalities: A Dynamic Quality-Aware Fusion Framework
Zhicheng Zhao, Yuancheng Xu, Andong Lu +2
Optical and Synthetic Aperture Radar (SAR) fusion-based object detection has attracted significant research interest in remote sensing, as these modalities provide complementary in…
Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
Zhicheng Zhao, Yin Huang, Lingma Sun +2
Tiny object detection in remote sensing imagery has attracted significant research interest in recent years. Despite recent progress, achieving balanced detection performance acros…
Mix-modal Federated Learning for MRI Image Segmentation
Guyue Hu, Siyuan Song, Jingpeng Sun +3
Magnetic resonance imaging (MRI) image segmentation is crucial in diagnosing and treating many diseases, such as brain tumors. Existing MRI image segmentation methods mainly fall i…
Federated Client-tailored Adapter for Medical Image Segmentation
Guyue Hu, Siyuan Song, Yukun Kang +4
Medical image segmentation in X-ray images is beneficial for computer-aided diagnosis and lesion localization. Existing methods mainly fall into a centralized learning paradigm, wh…
DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model
Zhicheng Zhao, Jinquan Yan, Chenglong Li +2
Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, which traditional single-image de…