RIS-LAD：面向低空无人机图像分割的基准与模型
RIS-LAD: A Benchmark and Model for Referring Low-Altitude Drone Image Segmentation
AI总结：
针对低空无人机图像分割中视角多样、对象密集等挑战，提出首个细粒度基准RIS-LAD及语义感知自适应推理网络SAARN，通过分解语义信息并路由至不同网络阶段，有效提升复杂场景下的分割性能。
摘要：
指代图像分割（RIS）旨在根据自然语言描述分割特定对象，在视觉-语言理解中扮演着重要角色。尽管RIS在遥感应用中取得了进展，但在低空无人机（LAD）场景下的RIS仍未得到充分探索。现有数据集和方法通常针对高空和静态视角图像设计，难以应对LAD视角的独特特征，如多样化的视角和高对象密度。为填补这一空白，我们提出了RIS-LAD，这是首个针对LAD场景的细粒度RIS基准。该数据集包含从真实无人机视频中收集的13,871个精心标注的图像-文本-掩码三元组，重点关注小型、杂乱和多视角场景。它突出了以往基准中不存在的新挑战，例如由微小对象引起的类别漂移以及拥挤同类对象下的对象漂移。为解决这些问题，我们提出了语义感知自适应推理网络（SAARN）。SAARN并非统一注入所有语言特征，而是将语义信息分解并路由到网络的不同阶段。具体而言，类别主导的语言增强（CDLE）在早期编码阶段将视觉特征与对象类别对齐，而自适应推理融合模块（ARFM）则跨尺度动态选择语义线索，以改善复杂场景中的推理。实验评估表明，RIS-LAD对最先进的RIS算法构成了重大挑战，同时也证明了我们提出的模型在应对这些挑战方面的有效性。数据集和代码将很快在https://github.com/AHideoKuzeA/RIS-LAD/公开。
英文摘要：
Referring Image Segmentation (RIS), which aims to segment specific objects based on natural language descriptions, plays an essential role in vision-language understanding. Despite its progress in remote sensing applications, RIS in Low-Altitude Drone (LAD) scenarios remains underexplored. Existing datasets and methods are typically designed for high-altitude and static-view imagery. They struggle to handle the unique characteristics of LAD views, such as diverse viewpoints and high object density. To fill this gap, we present RIS-LAD, the first fine-grained RIS benchmark tailored for LAD scenarios. This dataset comprises 13,871 carefully annotated image-text-mask triplets collected from realistic drone footage, with a focus on small, cluttered, and multi-viewpoint scenes. It highlights new challenges absent in previous benchmarks, such as category drift caused by tiny objects and object drift under crowded same-class objects. To tackle these issues, we propose the Semantic-Aware Adaptive Reasoning Network (SAARN). Rather than uniformly injecting all linguistic features, SAARN decomposes and routes semantic information to different stages of the network. Specifically, the Category-Dominated Linguistic Enhancement (CDLE) aligns visual features with object categories during early encoding, while the Adaptive Reasoning Fusion Module (ARFM) dynamically selects semantic cues across scales to improve reasoning in complex scenes. The experimental evaluation reveals that RIS-LAD presents substantial challenges to state-of-the-art RIS algorithms, and also demonstrates the effectiveness of our proposed model in addressing these challenges. The dataset and code will be publicly released soon at: https://github.com/AHideoKuzeA/RIS-LAD/.
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。
