LIU Xin1,2, ZHOU Kairui1,2, He Yulin3, JING Liping1,2, YU Jian1,2
1. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044 2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044 3. Beijing Newlink Technology Co., Ltd., Beijing 100083
Abstract：Few-shot learning aims to make machines recognize and summarize things by learning from a small number of samples like humans. The metric-based few-shot learning method is designed to learn a low-dimensional embedding space and query samples can be classified based on a distance between the query samples and the class embeddings in this space. In this paper, the key issues, class representation learning and similarity learning , are discussed to sort out the relevant literature. Only metric-based few-shot learning methods are classified in a detailed and comprehensive way, and they are classified from the perspective of key issues. Finally, the experimental results of current representative research on commonly used image classification datasets are summarized, the problems of the existing methods are analyzed, and the future research is prospected.
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