Frontiers of Data and Domputing ›› 2021, Vol. 3 ›› Issue (6): 17-34.doi: 10.11871/jfdc.issn.2096-742X.2021.06.002

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A Survey on Few-Shot Image Semantic Segmentation

CHEN Qiong(),YANG Yong(),HUANG Tianlin(),FENG Yuan   

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China
  • Received:2021-11-11 Online:2021-12-20 Published:2022-01-26
  • Contact: CHEN Qiong E-mail:csqchen@scut.edu.cn;csqchen@scut.edu.cn;202021044116@mail.scut.edu.cn;202121044681@mail.scut.edu.cn

Abstract:

[Objective] This paper introduces the few-shot image semantic segmentation methods systematically and comprehensively, as a reference to the design of the few-shot segmentation algorithm.[Methods] The metric-based meta-learning method is employed to perform the few-shot segmentation tasks. According to whether the metric tool is learnable, the few-shot segmentation algorithms are divided into prototype-based methods and parameter-based methods. This paper describes the advantages and disadvantages of both algorithms. [R-esults]Some classical and recent research works about the few-shot image semantic segmentation are analyzed in detail, together with an introduction of the main applications of the few-shot segmentation algorithm. [Conclusions] Hereinafter, the future development direction and trend of few-shot image segmentation are discussed, and its key problems and challenges are analyzed.

Key words: computer vision, image data, deep learning, image segmentation, few-shot learning, few-shot segmentation