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arXiv Computer Vision By Jing Huang, Duanchu Wang, Junjie Yang, Zihang Cheng, Cheng Li, Lin Cui, Zhouyi Wu, Di Wang

Beyond Appearance: Can Multimodal Large Language Models Exploit Vertical Structure for Remote Sensing Natural Scene Understanding?

Read the original on arXiv Computer Vision →

The paper introduces VertiCue-Bench, a diagnostic benchmark designed to test whether multimodal large language models (MLLMs) can perceive, ground, and utilize vertical structure information in remote-sensing natural scenes. It presents a three-stage framework—Perception, Grounding, Utilization—and a Representation Intervention Spectrum across various modalities to evaluate ten state-of-the-art models. The study finds a significant Vertical Structure Utilization Gap: while models show some geometric perception, they struggle to accurately link vertical evidence to spatial entities and incorporate it into semantic decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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