An Applicative Survey on Few-shot Learning

Background: Learning discriminative representation from large-scale data sets has made a breakthrough in decades. However, it is still a thorny problem to generate representative embedding from limited examples, for example, a class containing only one image. Recently, deep learning- based Few-Shot Learning (FSL) has been proposed. It tackles this problem by leveraging prior knowledge in various ways. Objective: In this work, we review recent advances of FSL in a perspective of high-dimensional representation learning. The results of the analysis can provide insights and directions for future work. Methods: We first present the definition of general FSL. Then, we propose a general framework for the FSL problem and give the taxonomy under the framework. We survey two FSL directions: learning policy and meta-learning. Results: We review the advanced applications of FSL, including image classification, object detection, image segmentation and other tasks etc., as well as the corresponding benchmarks to provide an overview of recent progress. Conclusion: In future work, FSL needs to be further studied in medical images, language models and reinforcement learning. In addition, cross-domain FSL, successive FSL and associated FSL are more challenging and valuable research directions.