Unfine-tuned MLLMs outperform fine-tuned models on remote sensing image captioning when captions are scored by their ability to reconstruct the source image, and a training-free self-correction method achieves SOTA performance.
Exploring a fine-grained multiscale method for cross-modal remote sensing image retrieval
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Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
A two-stage FTF retrieval system for remote sensing images and text achieves competitive accuracy with substantially higher efficiency by separating fast candidate recall from fine-grained reranking.
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Evaluating Remote Sensing Image Captions Beyond Metric Biases
Unfine-tuned MLLMs outperform fine-tuned models on remote sensing image captioning when captions are scored by their ability to reconstruct the source image, and a training-free self-correction method achieves SOTA performance.
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Sentinel2Cap: A Human-Annotated Benchmark Dataset for Multimodal Remote Sensing Image Captioning
Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
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Fast-then-Fine: A Two-Stage Framework with Multi-Granular Representation for Cross-Modal Retrieval in Remote Sensing
A two-stage FTF retrieval system for remote sensing images and text achieves competitive accuracy with substantially higher efficiency by separating fast candidate recall from fine-grained reranking.