GRE Suite：通过微调视觉语言模型与增强推理链进行地理定位推断
GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains
AI总结：
针对地理定位缺乏推理与可解释性的问题，提出GRE套件，含GRE30K数据集、多阶段推理GRE模型及GREval-Bench基准，显著提升各粒度定位精度。
摘要：
视觉语言模型（VLMs）的最新进展在视觉推理任务中展现了卓越的性能。然而，地理定位提出了独特的挑战，要求从图像中提取多粒度视觉线索，并将其与外部世界知识相结合进行系统推理。当前地理定位任务的方法往往缺乏稳健的推理机制和可解释性，限制了其有效性。为解决这些局限性，我们提出了地理推理增强（GRE）套件，这是一个新颖的框架，通过结构化推理链增强VLMs，以实现准确且可解释的位置推断。GRE套件系统地围绕三个关键维度开发：数据集、模型和基准。首先，我们引入了GRE30K，一个高质量的地理定位推理数据集，旨在促进细粒度的视觉和上下文分析。接下来，我们提出了GRE模型，该模型采用多阶段推理策略，逐步推断场景属性、局部细节和语义特征，从而以更高的精度缩小潜在地理区域。最后，我们构建了地理推理评估基准（GREval-Bench），这是一个全面的评估框架，在多样化的城市、自然和地标场景中评估VLMs，以衡量粗粒度（例如，国家、大洲）和细粒度（例如，城市、街道）的定位性能。实验结果表明，GRE在所有粒度的地理定位任务中显著优于现有方法，强调了推理增强型VLMs在复杂地理推断中的有效性。代码和数据将在https://github.com/Thorin215/GRE发布。
英文摘要：
Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mechanisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo Reason Enhancement (GRE) Suite, a novel framework that augments VLMs with structured reasoning chains for accurate and interpretable location inference. The GRE Suite is systematically developed across three key dimensions: dataset, model, and benchmark. First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. Next, we present the GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision. Finally, we construct the Geo Reason Evaluation Benchmark (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across diverse urban, natural, and landmark scenes to measure both coarse-grained (e.g., country, continent) and fine-grained (e.g., city, street) localization performance. Experimental results demonstrate that GRE significantly outperforms existing methods across all granularities of geo-localization tasks, underscoring the efficacy of reasoning-augmented VLMs in complex geographic inference. Code and data will be released at https://github.com/Thorin215/GRE.
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。
