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,
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
BMCR uses RL to adaptively compose modules from CNN and ViT backbones with an OT alignment interface, reporting mAP gains of up to 2.5 points on DOTA and DIOR-R datasets.
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.
citing papers explorer
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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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BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection
BMCR uses RL to adaptively compose modules from CNN and ViT backbones with an OT alignment interface, reporting mAP gains of up to 2.5 points on DOTA and DIOR-R datasets.
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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.