AeroReformer: Aerial Referring Transformer for UAV-based Referring Image Segmentation

As a novel and challenging task, referring segmentation combines computer vision and natural language processing to localise and segment objects based on textual descriptions. While Referring Image Segmentation (RIS) has been extensively studied in natural images, little attention has been given to aerial imagery, particularly from Unmanned Aerial Vehicles (UAVs). The unique challenges of UAV imagery, including complex spatial scales, occlusions, and varying object orientations, render existing RIS approaches ineffective. A key limitation has been the lack of UAV-specific datasets, as manually annotating pixel-level masks and generating textual descriptions is labour-intensive and time-consuming. To address this gap, we design an automatic labelling pipeline that leverages pre-existing UAV segmentation datasets and the Multimodal Large Language Models (MLLM) for generating textual descriptions. Furthermore, we propose Aerial Referring Transformer (AeroReformer), a novel framework for UAV Referring Image Segmentation (UAV-RIS), featuring a Vision-Language Cross-Attention Module (VLCAM) for effective cross-modal understanding and a Rotation-Aware Multi-Scale Fusion (RAMSF) decoder to enhance segmentation accuracy in aerial scenes. Extensive experiments on two newly developed datasets demonstrate the superiority of AeroReformer over existing methods, establishing a new benchmark for UAV-RIS. The datasets and code are publicly available at https://github.com/lironui/AeroReformer . • Introduce the UAV-specific dataset for referring image segmentation. • Design an automatic dataset pipeline using LLMs and open-source UAV data. • Propose AeroReformer, a novel Transformer-based UAV-RIS model.