SFR-Net learns scale-frustum representations for semantic segmentation of ultra-wide area remote sensing images, reporting SOTA mIoU gains of 1.72% and 4.29% on GID and FBPS.
Dynamicvis: An efficient and general visual foundation model for remote sensing image understanding,
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HiSem adds bidirectional differential attention and a two-level hierarchical routing module with MoE to handle semantic granularity differences in remote sensing change captioning, reporting +7.52% BLEU-4 on WHU-CDC.
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SFR-Net: Learning Scale-Frustum Representations for Ultra-Wide Area Remote Sensing Image Segmentation
SFR-Net learns scale-frustum representations for semantic segmentation of ultra-wide area remote sensing images, reporting SOTA mIoU gains of 1.72% and 4.29% on GID and FBPS.
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HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning
HiSem adds bidirectional differential attention and a two-level hierarchical routing module with MoE to handle semantic granularity differences in remote sensing change captioning, reporting +7.52% BLEU-4 on WHU-CDC.