HALD-FSOD: Hierarchical Adaptive Learning with Asymmetric Margin and Loss-Aware Dynamic Weighting for Few-Shot Object Detection in Remote Sensing Imagery

Recently, few-shot object detection (FSOD) in remote sensing images (RSIs) has been attracting increasing research attention. However, mainstream FSOD methods for RSIs suffer from severe base-class performance degradation when adapting to novel classes. Meanwhile, most existing methods introduce additional learnable parameters, which limit their practical deployment in remote sensing applications. In this paper, we focus on lightweight high-performance FSOD in RSIs and propose HALD-FSOD, an enhanced two-stage fine-tuning approach (TFA) based detector that achieves remarkable novel-class performance improvement without forgetting pre-trained base-class knowledge. Through a comprehensive analysis of the TFA framework’s limitations in remote sensing scenarios, a hierarchical adaptive learning framework is proposed, which achieves an optimal balance between base-class knowledge preservation and novel-class feature adaptation. Considering the low inter-class separability of geospatial objects, we design an asymmetric margin-enhanced cosine classifier to learn a more discriminative decision boundary for novel classes. Furthermore, a loss-aware dynamic class weighting mechanism is developed to alleviate the learning imbalance between base and novel classes. Extensive experiments on DIOR and NWPU VHR 10.v2 datasets demonstrate that our proposed HALD-FSOD achieves competitive overall performance, with remarkable improvement in novel-class detection and negligible base-class degradation, without introducing any additional learnable parameters.