Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (4): 43-54.DOI: 10.3778/j.issn.1002-8331.2010-0127

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Research Progress of Image Semantic Segmentation Based on Fully Supervised Learning

YUAN Mingyang, HUANG Hongbo, ZHOU Changsheng   

  1. 1.School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China
    2.Institute of Computational Intelligence, Beijing Information Science and Technology University, Beijing 100192, China
  • Online:2021-02-15 Published:2021-02-06



  1. 1.北京信息科技大学 计算机学院,北京 100101
    2.北京信息科技大学 计算智能研究所,北京 100192


In recent years, as deep learning enters the field of computer vision, various deep learning image semantic segmentation methods that are superior to traditional methods have appeared one after another, including fully supervised learning, the segmentation effect of the method significantly exceeds that of weakly supervised learning method, the image semantic segmentation methods of fully supervised learning are divided into five categories, and the most representative methods of the various types are analyzed in detail, and the implementation processes of the core components of each method are emphasized. After that, this paper summarizes the mainstream data sets in the field of semantic segmentation, introduces the performance algorithm indicators, and compares the effects of various representative methods on the mainstream data sets, and finally looks forward to the future of semantic segmentation.

Key words: computer vision, image semantic segmentation, deep learning, semantic segmentation method, fully supervised learning



关键词: 计算机视觉, 图像语义分割, 深度学习, 语义分割方法, 全监督学习