Deep Learning for Visual Segmentation: A Review

被引:3
|
作者
Sun, Jiaxing [1 ]
Li, Yujie [2 ]
Lu, Huimin [3 ]
Kamiya, Tohru [3 ]
Serikawa, Seiichi [3 ]
机构
[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China
[2] Fukuoka Univ, Fac Engn, Fukuoka, Japan
[3] Kyushu Inst Technol, Sch Engn, Kitakyushu, Fukuoka, Japan
来源
2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020) | 2020年
关键词
Deep learning; Image segmentation; Video segmentation;
D O I
10.1109/COMPSAC48688.2020.00-84
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Big data-driven deep learning methods have been widely used in image or video segmentation. The main challenge is that a large amount of labeled data is required in training deep teaming models, which is important in real-world applications. To the best of our knowledge, there exist few researches in the deep learning-based visual segmentation. To this end, this paper summarizes the algorithms and current situation of image or video segmentation technologies based on deep learning and point out the future trends. The characteristics of segmentation that based on semi-supervised or unsupervised teaming, all of the recent novel methods are summarized in this paper. The principle, advantages and disadvantages of each algorithms are also compared and analyzed.
引用
收藏
页码:1256 / 1260
页数:5
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