Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that
combines computer vision and natural language processing, delineating specific
regions in aerial images as described by textual queries. Traditional Referring
Image Segmentation (RIS) approaches have been impeded by the complex spatial
scales and orientations found in aerial imagery, leading to suboptimal
segmentation results. To address these challenges, we introduce the Rotated
Multi-Scale Interaction Network (RMSIN), an innovative approach designed for
the unique demands of RRSIS. RMSIN incorporates an Intra-scale Interaction
Module (IIM) to effectively address the fine-grained detail required at
multiple scales and a Cross-scale Interaction Module (CIM) for integrating
these details coherently across the network. Furthermore, RMSIN employs an
Adaptive Rotated Convolution (ARC) to account for the diverse orientations of
objects, a novel contribution that significantly enhances segmentation
accuracy. To assess the efficacy of RMSIN, we have curated an expansive dataset
comprising 17,402 image-caption-mask triplets, which is unparalleled in terms
of scale and variety. This dataset not only presents the model with a wide
range of spatial and rotational scenarios but also establishes a stringent
benchmark for the RRSIS task, ensuring a rigorous evaluation of performance.
Our experimental evaluations demonstrate the exceptional performance of RMSIN,
surpassing existing state-of-the-art models by a significant margin. All
datasets and code are made available at this https URL

引用远程感应图像分割（RRSIS）是一个将计算机视觉和自然语言处理相结合的新挑战，根据文本查询在航空图像中划定特定区域。为了解决此挑战，我们引入了旋转多尺度交互网络（RMSIN），它包含了用于处理多尺度细节的内尺度交互模块（IIM）和用于在网络中一致地整合这些细节的跨尺度交互模块（CIM），此外，RMSIN 还采用适应性旋转卷积（ARC）来考虑物体的多样方向，极大地提高了分割准确性。通过对包含 17,402 个图像 - 标题 - 掩模三元组的数据集进行实验评估，RMSIN 表现出了超出现有最先进模型很大的性能优势。