arXiv:2608. 16142v1 Announce Type: cross Abstract: UAV on-board vision systems are widely used for different activities, including monitoring in no-fly zones.
By Alam Noor, Luis Almeida, Kai Li, Jiyan Wu, Miguel Guti\'errez Gait\'an, Eduardo Tovar
arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.
By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen
arXiv:2608. 09467v1 Announce Type: cross Abstract: Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments.
By Boxiong Wang, Hui Kang, Geng Sun, Jiahui Li, Chao Yu, Daxin Tian
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.
By Yang Li, Ruichen Zhang, Yinqiu Liu, Guangyuan Liu, Abbas Jamalipour, Xianbin Wang, Dong In Kim
The paper introduces CrossUAV, a benchmark for joint object detection and instance segmentation in UAV imagery, and proposes Cross-Granularity Socialized Collaboration (CGSC), a framework that regulates hierarchical interactions between tasks. CGSC progressively activates cross-task exchanges and adaptively adjusts interaction strength based on task contribution, aiming to reduce interference and exploit complementary coarse- and fine-grained knowledge. Experiments show consistent improvements on both detection and segmentation tasks, supporting the effectiveness of hierarchical dynamic interaction for cross-granularity collaboration.
By Xinjie Yao, Ruipu Zhao, Yunqi Zhu, Zhihe Fan, Zhoupeng Guo, Weihao Li, Zhen Wang, Qilong Wang, Pengfei Zhu
Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments. Although recent end-to-end UAV vision-language-action (UAV-VLA) policies reduce reliance on separately designed perception, planning, and control modules, their behavior-cloning objectives provide limited corrective supervision for interactive closed-loop execution.