UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents
论文探讨了通过融合物理-数字AI代理来解决无人机理解人类意图和飞行动态的问题,提出了5+5框架以实现适应性和持续进化的无人机自主性。
论文探讨了通过融合物理-数字AI代理来解决无人机理解人类意图和飞行动态的问题,提出了5+5框架以实现适应性和持续进化的无人机自主性。
该研究通过条件扩散模拟器结合稀疏观测数据来提高高分辨率气温降尺度精度,用于改善热浪灾害预测。
该研究提出一种音频-视觉同步框架,通过特征编码、交叉注意力融合等方法解决二语发音评估中语音与唇部动作时间同步问题。
为解决大模型在生成气象文本时数值幻觉问题,通过使用AFDBench及GRPO方法提升AI气象预报的准确性与专业性。
This work addresses the instability and lack of robustness in existing single-model weather forecasting approaches across spatiotemporal domains. To overcome these limitations, the authors propose AdaWeather, a novel framework that adaptively combines multiple probabilistic weather forecast models through online learning, dynamically generating a unified probabilistic prediction via a mixture-of-experts strategy. Notably, AdaWeather achieves, for the first time, a logarithmic regret bound relative to the best fixed mixture of experts, outperforming conventional methods that only compete against the single best expert. Experimental results on temperature forecasting demonstrate that AdaWeather significantly surpasses current state-of-the-art approaches, confirming its effectiveness and robustness in real-world forecasting scenarios.
论文探讨了通过融合物理-数字AI代理来解决无人机理解人类意图和飞行动态的问题,提出了5+5框架以实现适应性和持续进化的无人机自主性。
该研究通过条件扩散模拟器结合稀疏观测数据来提高高分辨率气温降尺度精度,用于改善热浪灾害预测。
该研究提出一种音频-视觉同步框架,通过特征编码、交叉注意力融合等方法解决二语发音评估中语音与唇部动作时间同步问题。
为解决大模型在生成气象文本时数值幻觉问题,通过使用AFDBench及GRPO方法提升AI气象预报的准确性与专业性。
This work addresses the instability and lack of robustness in existing single-model weather forecasting approaches across spatiotemporal domains. To overcome these limitations, the authors propose AdaWeather, a novel framework that adaptively combines multiple probabilistic weather forecast models through online learning, dynamically generating a unified probabilistic prediction via a mixture-of-experts strategy. Notably, AdaWeather achieves, for the first time, a logarithmic regret bound relative to the best fixed mixture of experts, outperforming conventional methods that only compete against the single best expert. Experimental results on temperature forecasting demonstrate that AdaWeather significantly surpasses current state-of-the-art approaches, confirming its effectiveness and robustness in real-world forecasting scenarios.