方治屿
江西理工大学信息工程学院 赣州 34100
基金项目:国家自然科学基金(61762046)，江西省自然科学基金(20161BAB212048)
详细信息
作者简介:兰红：女，1969年生，教授，硕士生导师，主要研究方向为计算机视觉、图像处理与模式识别
方治屿：男，1993年生，硕士生，研究方向为计算机视觉与深度学习
通讯作者:兰红 lanhong69@163.com
中图分类号:TN911.73; TP391.41计量
文章访问数:5931
HTML全文浏览量:2270
PDF下载量:325
被引次数:0
出版历程
收稿日期:2019-07-01
修回日期:2019-11-03
网络出版日期:2019-11-13
刊出日期:2020-06-04
Recent Advances in Zero-Shot Learning
Hong LAN,,Zhiyu FANG
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Funds:The National Natural Science Foundation of China (61762046), The Natural Science Foundation of Jiangxi Province (20161BAB212048)
摘要
摘要:深度学习在人工智能领域已经取得了非常优秀的成就，在有监督识别任务中，使用深度学习算法训练海量的带标签数据，可以达到前所未有的识别精确度。但是，由于对海量数据的标注工作成本昂贵，对罕见类别获取海量数据难度较大，所以如何识别在训练过程中少见或从未见过的未知类仍然是一个严峻的问题。针对这个问题，该文回顾近年来的零样本图像识别技术研究，从研究背景、模型分析、数据集介绍、实验分析等方面全面阐释零样本图像识别技术。此外，该文还分析了当前研究存在的技术难题，并针对主流问题提出一些解决方案以及对未来研究的展望，为零样本学习的初****或研究者提供一些参考。
关键词:零样本学习/
深度卷积神经网络/
视觉语义嵌入/
泛化零样本学习
Abstract:Deep learning has shown excellent performance in the field of artificial intelligence. In the supervised identification task, deep learning algorithms can achieve unprecedented recognition accuracy by training massive tagged data. However, owing to the high cost of labeling massive data and the difficulty of obtaining massive data of rare categories, it is still a serious problem how to identify unknown class that is rarely or never seen during training. In view of this problem, the researches of Zero-Shot Learning (ZSL) in recent years is reviewed and illustrated from the aspects of research background, model analysis, data set introduction and performance analysis in this article. Some solutions of mainstream problem and prospects of future research are provided. Meanwhile, the current technical problems of ZSL is analyzed, which can offer some references to beginners and researchers of ZSL.
Key words:Zero-Shot Learning (ZSL)/
Deep Convolutional Neural Networks (DCNN)/
Visual-semantic embedding/
generalized Zero-Shot Learning (gZSL)
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https://jeit.ac.cn/article/exportPdf?id=8ae8ab23-a052-47cf-990a-8e51816c9e9f
