  Presently, the task of few-shot object detection (FSOD) in remote sensing
images (RSIs) has become a focal point of attention. Numerous few-shot
detectors, particularly those based on two-stage detectors, face challenges
when dealing with the multiscale complexities inherent in RSIs. Moreover, these
detectors present impractical characteristics in real-world applications,
mainly due to their unwieldy model parameters when handling large amount of
data. In contrast, we recognize the advantages of one-stage detectors,
including high detection speed and a global receptive field. Consequently, we
choose the YOLOv7 one-stage detector as a baseline and subject it to a novel
meta-learning training framework. This transformation allows the detector to
adeptly address FSOD tasks while capitalizing on its inherent advantage of
lightweight. Additionally, we thoroughly investigate the samples generated by
the meta-learning strategy and introduce a novel meta-sampling approach to
retain samples produced by our designed meta-detection head. Coupled with our
devised meta-cross loss, we deliberately utilize "negative samples" that are
often overlooked to extract valuable knowledge from them. This approach serves
to enhance detection accuracy and efficiently refine the overall meta-learning
strategy. To validate the effectiveness of our proposed detector, we conducted
performance comparisons with current state-of-the-art detectors using the DIOR
and NWPU VHR-10.v2 datasets, yielding satisfactory results.
