MM-UAVBench: Evaluating MLLMs for Low-Altitude UAVs
Analysis
This paper introduces MM-UAVBench, a new benchmark designed to evaluate Multimodal Large Language Models (MLLMs) in the context of low-altitude Unmanned Aerial Vehicle (UAV) scenarios. The significance lies in addressing the gap in current MLLM benchmarks, which often overlook the specific challenges of UAV applications. The benchmark focuses on perception, cognition, and planning, crucial for UAV intelligence. The paper's value is in providing a standardized evaluation framework and highlighting the limitations of existing MLLMs in this domain, thus guiding future research.
Key Takeaways
- •MM-UAVBench is a new benchmark for evaluating MLLMs in low-altitude UAV scenarios.
- •The benchmark assesses perception, cognition, and planning capabilities.
- •Experiments reveal limitations of current MLLMs in this domain.
- •The benchmark uses real-world UAV data and includes over 5.7K questions.
Reference / Citation
View Original"Current models struggle to adapt to the complex visual and cognitive demands of low-altitude scenarios."