超高分辨率遥感多模态大语言模型基准
A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs
AI总结：
本文提出RSHR-Bench，一个超高分辨率遥感多模态大语言模型基准，通过高分辨率图像和多样化任务评估视觉理解与推理能力。
摘要：
多模态大语言模型（MLLMs）在现有遥感（RS）基准上展示了强大的感知和推理性能。然而，大多数先前的基准依赖于低分辨率图像，而一些高分辨率基准则存在推理任务设计上的缺陷。我们发现，仅文本的LLM可以在没有图像的情况下与多模态视觉-语言模型在遥感推理任务中竞争，揭示了当前基准与视觉理解评估意图之间的关键不匹配。为实现忠实评估，我们引入RSHR-Bench，一个用于遥感视觉理解和推理的超高清基准。RSHR-Bench包含5,329张全场景图像，长边至少为4,000像素，每张图像可达约3 x 10^8像素，来源广泛使用的遥感语料库和无人机集合。我们设计了四个任务家族：多选VQA、开放式VQA、图像描述生成和单图像评估。这些任务覆盖了九个感知类别和四种推理类型，支持多轮和多图像对话。为了减少对语言先验的依赖，我们应用了强LLM的对抗过滤后，再进行严格的真人验证。总体而言，我们构建了3,864个VQA任务、3,913个图像描述生成任务以及500个完全人工编写或验证的单图像评估VQA对。在开源、闭源和专门针对遥感的VLMs上的评估揭示了在超高清分辨率场景中持续存在的性能差距。代码：https://github.com/Yunkaidang/RSHR
英文摘要：
Multimodal large language models (MLLMs) demonstrate strong perception and reasoning performance on existing remote sensing (RS) benchmarks. However, most prior benchmarks rely on low-resolution imagery, and some high-resolution benchmarks suffer from flawed reasoning-task designs. We show that text-only LLMs can perform competitively with multimodal vision-language models on RS reasoning tasks without access to images, revealing a critical mismatch between current benchmarks and the intended evaluation of visual understanding. To enable faithful assessment, we introduce RSHR-Bench, a super-high-resolution benchmark for RS visual understanding and reasoning. RSHR-Bench contains 5,329 full-scene images with a long side of at least 4,000 pixels, with up to about 3 x 10^8 pixels per image, sourced from widely used RS corpora and UAV collections. We design four task families: multiple-choice VQA, open-ended VQA, image captioning, and single-image evaluation. These tasks cover nine perception categories and four reasoning types, supporting multi-turn and multi-image dialog. To reduce reliance on language priors, we apply adversarial filtering with strong LLMs followed by rigorous human verification. Overall, we construct 3,864 VQA tasks, 3,913 image captioning tasks, and 500 fully human-written or verified single-image evaluation VQA pairs. Evaluations across open-source, closed-source, and RS-specific VLMs reveal persistent performance gaps in super-high-resolution scenarios. Code: https://github.com/Yunkaidang/RSHR
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
