RefDrone：一种针对无人机场景中指称表达理解的挑战性基准
RefDrone: A Challenging Benchmark for Referring Expression Comprehension in Drone Scenes
AI总结：
RefDrone提出了一种针对无人机场景中指称表达理解的挑战性基准，通过RDAgent和NGDINO方法提升了多目标和无目标情况下的识别性能。
摘要：
无人机已成为广泛应用的人形机器人平台，展现出在具身人工智能（Embodied AI）中的巨大潜力。指称表达理解（REC）使无人机能够根据自然语言表达来定位物体，这是具身人工智能的关键能力。尽管在地面场景中REC取得了显著进展，但空中视角引入了独特的挑战，包括视角变化、遮挡和尺度变化。为解决这一差距，我们引入了RefDrone，一种专门针对无人机场景的REC基准。RefDrone揭示了REC的三个关键挑战：1）多尺度和小尺度目标检测；2）多目标和无目标样本；3）复杂环境中的丰富上下文表达。为了高效构建该数据集，我们开发了RDAgent（具有多智能体系统的指称无人机标注框架），一种半自动化标注工具用于REC任务。RDAgent确保高质量的上下文表达并降低标注成本。此外，我们提出了Number GroundingDINO（NGDINO），一种新的方法，专门处理多目标和无目标情况。NGDINO明确学习并利用表达中所指的物体数量。与最先进的REC方法进行的全面实验表明，NGDINO在提出的RefDrone和现有的gRefCOCO数据集上均取得了优越的性能。数据集和代码将在https://github.com/sunzc-sunny/refdrone上公开。
英文摘要：
Drones have become prevalent robotic platforms with diverse applications, showing significant potential in Embodied Artificial Intelligence (Embodied AI). Referring Expression Comprehension (REC) enables drones to locate objects based on natural language expressions, a crucial capability for Embodied AI. Despite advances in REC for ground-level scenes, aerial views introduce unique challenges including varying viewpoints, occlusions and scale variations. To address this gap, we introduce RefDrone, a REC benchmark for drone scenes. RefDrone reveals three key challenges in REC: 1) multi-scale and small-scale target detection; 2) multi-target and no-target samples; 3) complex environment with rich contextual expressions. To efficiently construct this dataset, we develop RDAgent (referring drone annotation framework with multi-agent system), a semi-automated annotation tool for REC tasks. RDAgent ensures high-quality contextual expressions and reduces annotation cost. Furthermore, we propose Number GroundingDINO (NGDINO), a novel method designed to handle multi-target and no-target cases. NGDINO explicitly learns and utilizes the number of objects referred to in the expression. Comprehensive experiments with state-of-the-art REC methods demonstrate that NGDINO achieves superior performance on both the proposed RefDrone and the existing gRefCOCO datasets. The dataset and code are be publicly at https://github.com/sunzc-sunny/refdrone.
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。
