AirVista-II：面向动态场景语义理解的具身无人机智能体系统
AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding
AI总结：
提出AirVista-II端到端智能体系统，通过集成智能体任务识别调度、多模态感知和差异化关键帧提取策略，实现零样本下无人机动态场景的通用语义理解与推理。
摘要：
无人机在物流运输和灾难响应等动态环境中日益重要。然而，当前任务通常依赖人类操作员监控航拍视频并做出操作决策。这种人机协作模式在效率和适应性方面存在显著局限性。本文提出AirVista-II——一种面向具身无人机的端到端智能体系统，旨在实现动态场景中的通用语义理解和推理。该系统集成了基于智能体的任务识别与调度、多模态感知机制，以及针对不同时间场景定制的差异化关键帧提取策略，从而高效捕获关键场景信息。实验结果表明，所提系统在零样本设置下，能够在多种基于无人机的动态场景中实现高质量的语义理解。
英文摘要：
Unmanned Aerial Vehicles (UAVs) are increasingly important in dynamic environments such as logistics transportation and disaster response. However, current tasks often rely on human operators to monitor aerial videos and make operational decisions. This mode of human-machine collaboration suffers from significant limitations in efficiency and adaptability. In this paper, we present AirVista-II -- an end-to-end agentic system for embodied UAVs, designed to enable general-purpose semantic understanding and reasoning in dynamic scenes. The system integrates agent-based task identification and scheduling, multimodal perception mechanisms, and differentiated keyframe extraction strategies tailored for various temporal scenarios, enabling the efficient capture of critical scene information. Experimental results demonstrate that the proposed system achieves high-quality semantic understanding across diverse UAV-based dynamic scenarios under a zero-shot setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
