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

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Abstract
Few-shot object detection focuses on accurately identifying and locating novel categories with just a small set of annotated examples. However, accurate object detection in remote sensing images still faces challenges such as scale variations, small objects, and complex background interferences. To alleviate these issues, we propose DAFSDet, a dual attention guided few-shot object detection framework, to effectively mine discriminative key features. Specifically, we propose a Content-Aware Strip Pyramid (CASP) module that uses content-aware upsampling for spatial attention and bidirectional strip convolution for long-range context, forming a joint spatial–semantic attention mechanism. CASP produces robust multi-scale features, highlighting key regions while preserving semantic and contextual information for few-shot detection. Subsequently, we design a Deformable Attention Region Proposal Network (DA-RPN) to produce high-quality candidate regions from the enhanced features. Through the collaborative optimization of CASP and DA-RPN, our method significantly improves the accuracy and robustness of few-shot object detection in remote sensing images, particularly in complex scenarios with large number of small objects and messy backgrounds. Experimental results on two large-scale datasets, DIOR and NWPU VHR-10, demonstrate that the proposed model achieves strong performance and clear advantages.

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