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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2026 3verdicts
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Delta-LLaVA adds Change-Enhanced Attention, Change-SEG with prior embeddings, and Local Causal Attention to MLLMs to overcome temporal blindness, outperforming general models on a new unified benchmark for bi- and tri-temporal remote sensing tasks.
A unified cost-aware formulation couples fine-grained high-resolution sampling decisions with cross-patch representation prediction to achieve superior performance-cost trade-offs on remote sensing recognition and retrieval tasks using a new 10M-image benchmark.
citing papers explorer
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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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Decoding the Delta: Unifying Remote Sensing Change Detection and Understanding with Multimodal Large Language Models
Delta-LLaVA adds Change-Enhanced Attention, Change-SEG with prior embeddings, and Local Causal Attention to MLLMs to overcome temporal blindness, outperforming general models on a new unified benchmark for bi- and tri-temporal remote sensing tasks.
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Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding
A unified cost-aware formulation couples fine-grained high-resolution sampling decisions with cross-patch representation prediction to achieve superior performance-cost trade-offs on remote sensing recognition and retrieval tasks using a new 10M-image benchmark.