DAFSDet: Dual-Attention Guided Few-Shot Object Detection in Remote Sensing Images

Few-shot object detection aims to accurately identify and localize novel categories using only a small number of labeled samples. In remote sensing images, however, this task faces significant challenges due to substantial variations in target scale and complex backgrounds. To address these issues, this paper proposes a dual-attention guided few-shot object detection framework, DAFSDet. Specifically, a dual-attention strategy is implemented across the feature modeling and proposal generation stages. For feature fusion, the Content-Aware Strip Pyramid (CASP) is designed to enhance multi-scale feature representation by modeling spatial and contextual information. In the detection stage, a Deformable Attention RPN (DA-RPN) is proposed to improve the localization quality of candidate regions. With these designs, the proposed method effectively mitigates the challenges posed by multi-scale variations and complex backgrounds. Experimental results on the DIOR and NWPU VHR-10 datasets demonstrate consistent improvements over baseline methods, with notable gains of 7.54 mAP on DIOR Split 2 under the 10-shot setting and 2.09 mAP on NWPU VHR-10 under the 3-shot setting. These results indicate that the proposed method offers an effective solution for few-shot object detection in complex remote sensing scenarios.