Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (2): 1-11.DOI: 10.3778/j.issn.1002-8331.2205-0496

• Research Hotspots and Reviews • Previous Articles     Next Articles

Survey on Image Semantic Segmentation in Dilemma of Few-Shot

WEI Ting, LI Xinlei, LIU Hui   

  1. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China
  • Online:2023-01-15 Published:2023-01-15



  1. 上海对外经贸大学 统计与信息学院,上海 201620

Abstract: In recent years, image semantic segmentation has developed rapidly due to the emergence of large-scale datasets. However, in practical applications, it is not easy to obtain large-scale, high-quality images, and image annotation also consumes a lot of manpower and time costs. In order to get rid of the dependence on the number of samples, few-shot semantic segmentation has gradually become a research hotspot. The current few-shot semantic segmentation methods mainly use the idea of meta-learning, which can be divided into three categories:based on the siamese neural network, based on the prototype network and based on the attention mechanism according to different model structures. Based on the current research, this paper introduces the development, advantages and disadvantages of various methods for few-shot semantic segmentation, as well as common datasets and experimental designs. On this basis, the application scenarios and future development directions are summarized.

Key words: semantic segmentation, meta-learning, few-shot learning

摘要: 近年来,由于大规模数据集的出现,图像语义分割技术得到快速发展。但在实际场景中,并不容易获取到大规模、高质量的图像,图像的标注也需要消耗大量的人力和时间成本。为了摆脱对样本数量的依赖,小样本语义分割技术逐渐成为研究热点。当前小样本语义分割的方法主要利用了元学习的思想,按照不同的模型结构可划分为基于孪生神经网络、基于原型网络和基于注意力机制三大类。基于近年来小样本语义分割的发展现状,介绍了小样本语义分割各类方法的发展及优缺点,以及小样本语义分割任务中常用的数据集及实验设计。在此基础上,总结了小样本语义分割技术的应用场景及未来的发展方向。

关键词: 语义分割, 元学习, 小样本学习