Few-shot object detection based on global context and implicit knowledge decoupled head

The acquisition cycle of remote sensing images is slow, and the labelling process encounters challenges, which have become prominent with the rapid development of remote sensing image object detection research. Therefore, this article provides a way to make the model better capture the diversity and contextual relationships in the data, and solve this problem by more than just data augmentation. Specifically, this method is a few-shot object detection method for remote sensing based on global context combined with implicit knowledge decoupled head (GC-IKDH). This method first uses a segmentation strategy to convert high-resolution images into low-resolution images and expands the sample size through a generative model. Secondly, GC attention is introduced to generate a GC vector by weighting and averaging the information of each position in the input sequence, which helps the model better understand the semantics of the input sequence. Finally, an IKDH is added to improve the model head, which is used to learn specific features in the data so that the model can better handle the diversity in the data. Experimental results show that GC attention and IKDH boosting provide a good performance boost to the baseline model. Compared with other few-shot samples, this method achieves state-of-the-art performance under different shot settings and highly competitive results on two benchmark datasets (NWPU VHR-10 and DIOR).