ERGeoBench：マルチモーダル大規模言語モデルにおける身体化推論と地理的位置特定のための包括的ベンチマーク
ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
身体化された地理的位置特定能力を評価する新しいベンチマークERGeoBenchを提案し、現在のMLLMが高レベルの地理的意味は推論できるが、細かい知覚や空間的一貫性に課題があることを示した。
著者: Kaiwen Xue, Tao Wei, Guoxin Zhang, Zhonghong Ou, Kaoyan Lu, Yu Feng, Yifan Zhu, Haoran Luo
分類: cs.CV, cs.AI
原文アブストラクト
Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a diagnostic benchmark for vision-driven embodied geo-localization. ERGeoBench evaluates models under three progressive settings -- single-view, panorama-view, and embodied-view -- where agents may actively acquire observations through sequential changes in yaw, pitch, and zoom. The benchmark contains 2,207 globally distributed street-view panoramas and measures four complementary capabilities: foundational perception, spatial awareness, common sense reasoning, and geo-localization reasoning. Evaluations of leading proprietary and open-source MLLMs show that current models can infer high-level geographic semantics, but still struggle with fine-grained perceptual operations, metric localization, and spatial consistency across views. We further observe that geo-localization is strongly correlated with the other capability dimensions, suggesting that accurate localization depends on integrated perception, spatial reasoning, and commonsense inference rather than isolated visual recognition. Overall, ERGeoBench provides a unified framework for diagnosing and advancing human-like embodied geo-localization. Project Page: https://kaixuewen.github.io/ERGeoBench/