GeoVLM-R1：强化微调以提升遥感推理能力
GeoVLM-R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning
AI总结：
本文提出GeoVLM-R1框架，通过任务感知奖励的强化微调，增强遥感图像的推理能力，在多个EO基准上超越现有视觉语言模型。
摘要：
近期强化学习（RL）的进展在自然图像领域展现了强大的推理能力，但其在地球观测（EO）领域的潜力尚未得到充分探索。EO任务引入了独特的挑战，涵盖指代目标检测、图像或区域描述、变化检测、基础定位和时间分析，这些任务需要具备任务感知的推理能力。我们提出了一种新颖的后训练框架，该框架整合了任务感知奖励，以实现基于推理的RL模型对多样化EO任务的有效适应。这种训练策略增强了遥感图像的推理能力，稳定了优化过程，并提升了鲁棒性。在多个EO基准上的大量实验表明，与最先进的通用和专用视觉语言模型相比，我们取得了持续的性能提升。代码和模型将公开发布于 https://mustansarfiaz.github.io/GeoVLM-R1/ 。
英文摘要：
Recent advances in reinforcement learning (RL) have delivered strong reasoning capabilities in natural image domains, yet their potential for Earth Observation (EO) remains largely unexplored. EO tasks introduce unique challenges, spanning referred object detection, image or region captioning, change detection, grounding, and temporal analysis, that demand task aware reasoning. We propose a novel post training framework that incorporates task aware rewards to enable effective adaptation of reasoning based RL models to diverse EO tasks. This training strategy enhances reasoning capabilities for remote sensing images, stabilizes optimization, and improves robustness. Extensive experiments across multiple EO benchmarks show consistent performance gains over state of the art generic and specialized vision language models. Code and models will be released publicly at https://mustansarfiaz.github.io/GeoVLM-R1/ .
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。
