用学习的评分模型自动筛选高质量遥感图文数据，提升视觉语言模型性能。
Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models
- 基于遥感图文偏好数据训练评分模型，自动评估合成数据质量。
- 用评分模型选前30%数据微调，精度优于全量数据或传统评分方法。
- 可应用于强化学习与测试阶段提升，适合遥感多模态研究者使用。
视觉语言模型（VLM）在语义引导下展现了解析遥感（RS）图像的巨大潜力。然而，其性能高度依赖于高质量的图文训练数据，该数据需准确捕捉视觉内容与语言描述间的丰富语义关系。与自然图像不同，遥感领域缺乏来自网络的大规模交错图文对，导致数据收集困难。现有方法主要依赖规则或主流VLM进行数据合成，但缺乏系统性的自动化质量评估框架。为此，我们提出一种基于大规模遥感图文偏好数据训练的评分模型，实现对合成遥感图文数据的质量自动评估。实验表明，使用本模型排序后前30%的数据微调CLIP或先进VLM（如Qwen2-VL），性能优于全量数据微调及基于CLIP-score的排名方法。此外，我们还展示了该评分模型在强化学习训练和最佳- N（BoN）测试时缩放中的应用，显著提升了遥感任务中VLM的表现。代码、模型与数据集均已公开。
原文摘要 · Abstract (English)
Vision-Language Models (VLMs) have demonstrated great potential in interpreting remote sensing (RS) images through language-guided semantic. However, the effectiveness of these VLMs critically depends on high-quality image-text training data that captures rich semantic relationships between visual content and language descriptions. Unlike natural images, RS lacks large-scale interleaved image-text pairs from web data, making data collection challenging. While current approaches rely primarily on rule-based methods or flagship VLMs for data synthesis, a systematic framework for automated quality assessment of such synthetically generated RS vision-language data is notably absent. To fill this gap, we propose a novel score model trained on large-scale RS vision-language preference data for automated quality assessment. Our empirical results demonstrate that fine-tuning CLIP or advanced VLMs (e.g., Qwen2-VL) with the top 30% of data ranked by our score model achieves superior accuracy compared to both full-data fine-tuning and CLIP-score-based ranking approaches. Furthermore, we demonstrate applications of our scoring model for reinforcement learning (RL) training and best-of-N (BoN) test-time scaling, enabling significant improvements in VLM performance for RS tasks. Our code, model, and dataset are publicly available
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