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Cross Modal Retrieval On Rsicd

Metrics

Image-to-text R@1
Mean Recall
text-to-image R@1

Results

Performance results of various models on this benchmark

Paper Title
GeoRSCLIP-FT21.13%38.87%15.59%RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
GLISA20.68%37.69%14.73%Global–Local Information Soft-Alignment for Cross-Modal Remote-Sensing Image–Text Retrieval
HarMA (w/ GeoRSCLIP)20.52%38.95%15.84%Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment
RemoteCLIP18.39%36.35%14.73%RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
PE-RSITR (MRS-Adapter)14.13%31.12%11.63%Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval
PIR9.88%24.46%6.97%A Prior Instruction Representation Framework for Remote Sensing Image-text Retrieval
DOVE8.66%22.72%6.04%Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval
SWAN7.41%20.61%5.56%Reducing Semantic Confusion: Scene-aware Aggregation Network for Remote Sensing Cross-modal Retrieval
GaLR6.59%18.96%4.69%Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
AMFMN5.21%15.53%4.08%Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
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Cross Modal Retrieval On Rsicd | SOTA | HyperAI