TAR uses frozen text encoders on remote sensing scene descriptions to boost high-level features for coarse-to-fine optical-SAR image registration under large deformations.
Aslfeat: Learning local features of accurate shape and localization
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GESS introduces joint semantic-normal and depth stability prediction heads, the SDAK keypoint mechanism, and the UTCF descriptor fusion module to leverage multi-cue synergy for improved robustness and discriminability.
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TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images
TAR uses frozen text encoders on remote sensing scene descriptions to boost high-level features for coarse-to-fine optical-SAR image registration under large deformations.
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GESS: Multi-cue Guided Local Feature Learning via Geometric and Semantic Synergy
GESS introduces joint semantic-normal and depth stability prediction heads, the SDAK keypoint mechanism, and the UTCF descriptor fusion module to leverage multi-cue synergy for improved robustness and discriminability.