Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

📰 ArXiv cs.AI

Learn to optimize onboard vision-language inference in UAVs using LLM-enhanced optimization for efficient low-altitude economy networks

advanced Published 9 Jun 2026

Action Steps

Implement LLM-enhanced optimization techniques to reduce computational complexity in onboard VLMs
Configure UAVs with vision-language models for real-time multimodal inference
Apply optimization algorithms to balance inference accuracy and communication efficiency
Test and evaluate the performance of LLM-enhanced VLMs in LAENets
Deploy optimized VLMs on UAVs for efficient onboard inference

Who Needs to Know This

Computer vision engineers, AI researchers, and UAV system designers can benefit from this knowledge to improve the accuracy and efficiency of their systems

Key Insight

💡 LLM-enhanced optimization can significantly improve the efficiency of onboard vision-language inference in UAVs

Key Takeaways

Learn to optimize onboard vision-language inference in UAVs using LLM-enhanced optimization for efficient low-altitude economy networks

Full Article

Title: Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

Abstract:
arXiv:2510.10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection. To support these scenarios, unmanned aerial vehicles (UAVs) equipped with onboard vision-language models (VLMs) offer a promising solution for real-time multimodal inference. However, ensuring both inference accuracy and communication efficiency remains

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