RSRCC is a new 126k-question benchmark for fine-grained remote sensing change question-answering, constructed via a hierarchical semi-supervised pipeline with retrieval-augmented Best-of-N ranking.
Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. We further evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. Our work aims to significantly contribute to the development of advanced vision-language models in the field of remote sensing. The data and code can be accessed at https://github.com/lx709/VRSBench.
representative citing papers
VRA is a training-free agentic framework that orchestrates off-the-shelf LVLMs with a reasoning model via iterative verification and refinement, raising accuracy on remote sensing VQA from 52.8% to 78.8% and delivering up to 40.67% gains on hard question types.
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
Remote-sensing foundation models need domain-specific design and evaluation around measurement physics and decision constraints; benchmark accuracy alone is insufficient for trustworthy EO deployment.
citing papers explorer
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RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking
RSRCC is a new 126k-question benchmark for fine-grained remote sensing change question-answering, constructed via a hierarchical semi-supervised pipeline with retrieval-augmented Best-of-N ranking.
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Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling
VRA is a training-free agentic framework that orchestrates off-the-shelf LVLMs with a reasoning model via iterative verification and refinement, raising accuracy on remote sensing VQA from 52.8% to 78.8% and delivering up to 40.67% gains on hard question types.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
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Scalable and Trustworthy Earth Observation Foundation Models
Remote-sensing foundation models need domain-specific design and evaluation around measurement physics and decision constraints; benchmark accuracy alone is insufficient for trustworthy EO deployment.