A survey on few-shot class-incremental learning

被引：0
|
作者：
Tian, Songsong [1 ,2 ,4 ]
Li, Lusi [5 ]
Li, Weijun [1 ,3 ,4 ]
Ran, Hang [1 ,4 ]
Ning, Xin [1 ,3 ,4 ]
Tiwari, Prayag [6 ]
机构：
[1] Institute of Semiconductors, Chinese Academy of Sciences, Beijing,100083, China
[2] School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing,100049, China
[3] School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing,100083, China
[4] Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing,100083, China
[5] Department of Computer Science, Old Dominion University, Norfolk,VA,23529, United States
[6] School of Information Technology, Halmstad University, Halmstad,30118, Sweden
来源：
Neural Networks | 2024年 / 169卷
关键词：
All Open Access; Hybrid Gold;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Image segmentation
引用
收藏
页码：307 / 324
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