Few-Shot Learning for Remote Sensing Image Retrieval With MAML

Few-shot remote sensing image retrieval is devoted to add new retrieval categories with a small number of labeled samples, and simultaneously achieve favorable retrieval performance for new categories and keep the primary retrieval performance for the original categories as far as possible. Few-shot learning has received considerable attention, however its applications in image retrieval, especially for remote sensing image retrieval, are still very few. In this paper, we redefine the few-shot image retrieval problem formally and further propose a few-shot retrieval method under model-agnostic meta-learning (MAML) framework, combined with ResNet and GeM as feature extraction module. Moreover, the optimal mean average precision (mAP) is used as ranking loss for defining the loss function of learning model, and a histogram binning approximation of mAP, which is differential, is thus employed so that the whole few-shot retrieval model can be end-to-end trained. The retrieval effectiveness and efficiency of our method are verified on UC-Merced and AID data sets.