Few-Shot Object Detection via Context-Aware Aggregation for Remote Sensing Images
Yong Zhou, Han Hu, Jiaqi Zhao, Hancheng Zhu, Rui Yao, Wenliang Du
Abstract
Few-shot object detection methods have made prodigious progress in recent years. However, these methods are designed for optical images at a single scale, which leads to significantly degraded detection performance due to object scale variation of remote sensing images. In this letter, we propose a few-shot object detection method for the problem of scale variation in remote sensing images. More specifically, our model contains two main components: a context-aware pixel aggregation (CPA) that allows the model to adapt to objects at different scales through different scale convolution and a context-aware feature aggregation (CFA) that enhances context awareness to obtain more semantic information through a graph convolution network (GCN). Experiments on the DIOR dataset demonstrate that our model can achieve a satisfying detection performance on remote sensing images, and our model performs significantly better than the state-of-the-art model.