  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 https://github.com/Lsan2401/RMSIN.
