arXiv AI
Aerial-R1: Reinforcing Aerial Reasoning and Segmentation in MLLMs
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Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System
arXiv:2608. 11738v1 Announce Type: cross Abstract: Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density.
arXiv Computer Vision
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Drone-PUR: From Visual Clue Discovery to Understanding and Reasoning in UAV Scenes
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Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
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arXiv Computer Vision
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GeoReason: Bridging Logical Reasoning and Spatial Fidelity in Remote Sensing Segmentation
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arXiv Computer Vision
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Beyond Localization: A Comprehensive Benchmark of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
The paper introduces PCSR-Bench, a benchmark of 84,373 question‑answer pairs derived from 2,600 omnidirectional images across 26 indoor environments, designed to evaluate perspective‑conditioned spatial reasoning (PCSR) in multimodal large language models (MLLMs). It reports a significant perception–reasoning gap, with accuracy dropping from 57.59% on limited field‑of‑view reasoning to as low as 0.64% on open‑ended compositional directional chains. An RL‑based diagnostic study on a 7B‑scale model shows that reward shaping can improve performance to 60.06% on a controlled task, indicating partial plasticity of PCSR capabilities.