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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	Repository
GeoRSCLIP-FT	21.13%	38.87%	15.59%	RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
GLISA	20.68%	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
RemoteCLIP	18.39%	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
PIR	9.88%	24.46%	6.97%	A Prior Instruction Representation Framework for Remote Sensing Image-text Retrieval	-
DOVE	8.66%	22.72%	6.04%	Direction-Oriented Visual-semantic Embedding Model for Remote Sensing Image-text Retrieval	-
SWAN	7.41%	20.61%	5.56%	Reducing Semantic Confusion: Scene-aware Aggregation Network for Remote Sensing Cross-modal Retrieval	-
GaLR	6.59%	18.96%	4.69%	Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
AMFMN	5.21%	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