零样本图像识别

兰红; 方治屿

doi:10.11999/JEIT190485

零样本图像识别

doi: 10.11999/JEIT190485

兰红^,,
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
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- 文章访问数: 7838
- HTML全文浏览量: 3524
- PDF下载量: 505
- 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，为零样本学习的初学者或研究者提供一些参考。
- 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.
- Zero-Shot Learning (ZSL) /
- Deep Convolutional Neural Networks (DCNN) /
- Visual-semantic embedding /
- generalized Zero-Shot Learning (gZSL)

HTML全文

图 1 零样本学习技术结构图

下载: 全尺寸图片幻灯片

图 2 零样本学习示意图

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图 3 经典归纳式零样本模型示意图^[7]

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图 4 AwA类-属性关系矩阵^[7]

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图 5 3种视觉-语义映射示意图

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图 6 领域漂移示例图^[55]

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图 7 语义间隔示例图

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表 1 机器学习方法对比表

	训练集$\{ \cal{X},\cal{Y}\} $	测试集$\{ \cal{X},\cal{Z}\} $	训练类$\cal{Y}$与测试类$\cal{Z}$间关系$R$	最终分类器$C$
无监督学习	大量无标签图片	已知类图片	$\cal{Y} = \cal{Z}$	$C:\cal{X} \to \cal{Y}$
有监督学习	大量带标签图片	已知类图片	$\cal{Y} = \cal{Z}$	$C:\cal{X} \to \cal{Y}$
半监督学习	较少带标签图片和大量无标签图片	已知类图片	$\cal{Y} = \cal{Z}$	$C:\cal{X} \to \cal{Y}$
少样本学习	极少带标签图片和大量无标签图片	已知类图片	$\cal{Y} = \cal{Z}$	$C:\cal{X} \to \cal{Y}$
零样本学习	大量带标签图片	未知类图片	${\cal Y} \cap {\cal Z} = \varnothing$	$C:\cal{X} \to \cal{Z}$

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表 2 零样本学习中深度卷积神经网络使用情况统计表

网络	论文数量
VGG	501
GoogleNet	271
ResNet	397

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表 3 零样本学习性能比较(%)

方法	传统零样本学习						泛化零样本学习
	AwA		CUB		SUN		AwA			CUB			SUN
	SS	PS	SS	PS	SS	PS	U→T	S→T	H	U→T	S→T	H	U→T	S→T	H
IAP	46.9	35.9	27.1	24.0	17.4	19.4	0.9	87.6	1.8	0.2	72.8	0.4	1.0	37.8	1.8
DAP	58.7	46.1	37.5	40.0	38.9	39.9	0.0	84.7	0.0	1.7	67.9	3.3	4.2	25.1	7.2
DeViSE	68.6	59.7	53.2	52.0	57.5	56.5	17.1	74.7	27.8	23.8	53.0	32.8	16.9	27.4	20.9
ConSE	67.9	44.5	36.7	34.3	44.2	38.8	0.5	90.6	1.0	1.6	72.2	3.1	6.8	39.9	11.6
SJE	69.5	61.9	55.3	53.9	57.1	53.7	8.0	73.9	14.4	23.5	59.2	33.6	14.7	30.5	19.8
SAE	80.7	54.1	33.4	33.3	42.4	40.3	1.1	82.2	2.2	7.8	54.0	13.6	8.8	18.0	11.8
SYNC	71.2	46.6	54.1	55.6	59.1	56.3	10.0	90.5	18.0	11.5	70.9	19.8	7.9	43.3	13.4
LDF	83.4	–	70.4	–	–	–	–	–	–	–	–	–	–	–	–
SP-AEN	–	58.5	–	55.4	–	59.2	23.3	90.9	37.1	34.7	70.6	46.6	24.9	38.6	30.3
QFSL	84.8	79.7	69.7	72.1	61.7	58.3	66.2	93.1	77.4	71.5	74.9	73.2	51.3	31.2	38.8

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