HPMF: Hypergraph-Guided Prototype Mining Framework for Few-Shot Object Detection in Remote Sensing Images

Few-shot object detection (FSOD) within remote sensing imagery has achieved great advancements in recent years. However, most existing methods are facing one key challenge while handling remote sensing images: many unlabeled instances in few-shot images are treated as background, which tends to degrade the generalization of the trained model severely. This article presents hypergraph-guided prototype mining framework (HPMF), an HPMF that addresses the challenge through joint optimization from three perspectives. The first is hierarchical reference mining (HRM) which constructs a class-instance dual-driven prototype space that enables mining the unlabeled instances via cross-hierarchical similarity fusion. The second is a robust pseudobox estimator (RPE) that generates high-quality pseudobounding boxes for the HRM-mined instances via adaptive density clustering and multistatistic aggregation. The third is a hypergraph-guided decoder (HGD) that introduces hypergraphs into the transformer decoder for group semantic modeling, enhancing high-order semantic association and similarity of instance features, thereby further improving the mining performance of the HRM module. Extensive experiments under various settings show that the proposed HPMF outperforms state-of-the-art methods consistently across multiple widely adopted remote sensing FSOD benchmarks such as DIOR, NWPU-VHR10 v2, and HRRSD.