Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models

• URL: http://arxiv.org/abs/2503.00743v1
• Date: Sun, 02 Mar 2025 05:44:56 GMT
• Title: Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models
• Authors: Dilxat Muhtar, Enzhuo Zhang, Zhenshi Li, Feng Gu, Yanglangxing He, Pengfeng Xiao, Xueliang Zhang,
• Abstract summary: We propose a novel score model trained on large-scale RS visionlanguage preference data for automated quality assessment.<n>Our empirical results demonstrate that fine-tuning CLIP or advanced VLMs with the top 30% of data ranked by our score model achieves superior interpretation accuracy.
• Score: 9.238739743596236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) have demonstrated great potential in interpreting remote sensing (RS) images through language-guided semantic understanding. 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 visionlanguage data is notably absent. To fill this gap, we propose a novel score model trained on large-scale RS visionlanguage 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 interpretation 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) testtime scaling, enabling significant improvements in VLM performance for RS tasks.

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