| Summary: | Object detection is crucial in aerial imagery analysis. Previous methods based on convolutional neural networks (CNNs) require large-scale labeled datasets for training to achieve significant success. However, the acquisition and manual annotation of such data is time-consuming and expensive. In this study, we present an original few-shot object detection (FSOD) method that focuses on detecting unseen objects in aerial imagery with limited labeled samples. Specifically, we revisited the multi-similarity network from deep metric learning and incorporated it into a faster region-CNN (R-CNN) architecture for FSOD, learning distinctive feature representations, and effectively improving the performance of unseen class samples. Furthermore, we preserved the knowledge learned from abundant base data by designing a knowledge inheritance module to ease the influence of catastrophic forgetting. We conducted experiments on two benchmark remote sensing image datasets, and the results demonstrated that the proposed methods could achieve a satisfactory performance for FSOD in aerial imagery.
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