arXiv Computer Vision
Drone-PUR: From Visual Clue Discovery to Understanding and Reasoning in UAV Scenes
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Aug 13
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 AI
Jun 29
SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion
arXiv:2606. 27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation.
arXiv AI
Oct 27
Aerial-R1: Reinforcing Aerial Reasoning and Segmentation in MLLMs
arXiv:2610.33056v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) have revolutionized general visual perception, existing methods suffer from severe hallucination in th...
arXiv AI
Jun 16
ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering
arXiv:2606. 14772v1 Announce Type: cross Abstract: Aerial Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (UAVs) to actively perceive the environment and answer natural language questions.
arXiv AI
Aug 11
GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views
arXiv:2608. 09270v1 Announce Type: cross Abstract: Fine-grained cross-modal understanding in drone views is essential for aerial vision-language navigation.
arXiv AI
Sep 10
Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots. The method also employs object‑conditioned visual reasoning and egocentric regional guidance, achieving state‑of‑the‑art success rates on the UAV‑ON benchmark.