Understanding Dynamic Scenes at Gigapixel Scale: Wide-Area Spatio-Temporal Perception from UAVs

arXiv:2609.18210 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-29

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• AI Integration — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Wide-area Spatio-temporal Scene Understanding — 1× in this paper

Sources

• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• YOLOv11: An Overview of the Key Architectural Enhancements
• YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
• Can Vision-Language Models Think from the Sky? Unifying UAV Reasoning and Generation
• MiniCPM-V: A GPT-4V Level MLLM on Your Phone
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
• UAV traffic scene understanding: A regulation embedded multi-modal network and a unified benchmark
• Objects as Points
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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