Few-Shot Object Detection on Remote Sensing Images based on Diverse Regional Feature Generation

Few-shot object detection on remote sensing images aims to detect novel objects with limited annotated samples. Data augmentation has become an effective approach in addressing the issue of few-shot object detection by increasing the number of samples. However, existing methods mainly employ data augmentation in the image domain and do not fully utilize the semantic feature information. In this paper, we introduce a method called diverse regional feature generation for few-shot object detection on remote sensing images, which introduces the WGAN model as the feature generator to generate diverse positive and negative features for the base and novel classes. Our method contains the intra-class diversity module and the inter-class dispersion module to train the classification head. Specifically, the intra-class diversity module is to generate diverse visual features for each category, while the inter-class dispersion module aims to push the features from different categories far away to model the real data distribution. The proposed algorithm is evaluated on the publicly available DIOR dataset and the experimental results show that our method significantly outperforms the other state-of-the-art methods.