小样本图像语义分割综述

doi:10.11871/jfdc.issn.2096-742X.2021.06.002

数据与计算发展前沿 ›› 2021, Vol. 3 ›› Issue (6): 17-34.

doi: 10.11871/jfdc.issn.2096-742X.2021.06.002

• 专刊:科学大数据挖掘与知识发现 • 上一篇下一篇

小样本图像语义分割综述

陈琼(),杨咏(),黄天林(),冯媛

华南理工大学,计算机科学与工程学院,广东广州 510006

收稿日期:2021-11-11 出版日期:2021-12-20 发布日期:2022-01-26
通讯作者: 陈琼
作者简介:陈琼, 华南理工大学计算机科学与工程学院,博士,硕士生导师,中国计算机学会会员、中国人工智能学会离散智能计算专委会委员,主要研究方向为机器学习、图像分类和分割、深度强化学习。
本文中负责制定论文框架、修订论文和总体统稿。
CHEN Qiong, Ph.D, is an associate professor and master supervisor of School of Computer Science and Technology, South China University of Technology. Her recent research interests include machine learning, imbalanced classification, image classification and segmentation, deep reinforcement learning.
In this paper, she is responsible for paper framework design, manuscript edition, and the final compilation. E-mail: csqchen@scut.edu.cn;|杨咏,华南理工大学计算机科学与工程学院,硕士研究生,研究方向为图像语义分割、小样本语义分割。
本文中负责小样本分割算法、摘要、引言等内容的撰写。
YANG Yong is a postgraduate student of School of Computer Science and Engi-neering, South China University of Technology. His research interests include image semantic segmentation and few-shot semantic segmentation.
In this paper, he is responsible for the parts of the few-shot segmentation algorithm, abstract, and introduction. E-mail: 202021044116@mail.scut.edu.cn;|黄天林,华南理工大学计算机科学与工程学院,硕士研究生,目前研究方向为小样本语义分割。
本文中负责修订论文、撰写小样本分割算法的应用。
HUANG Tianlin is a postgraduate stu-dent of School of Computer Science and Engineering, South China University of Technology. His recent research direction is few-shot semantic segmentation.
In this paper, he is responsible for revision of the manuscript and writing the part of the applications of few-shot segmen-tation algorithm. E-mail: 202121044681@mail.scut.edu.cn;|冯媛,华南理工大学计算机科学与工程学院,硕士研究生,研究方向为小样本图像分类、零样本图像分类、零样本语义分割。
本文中负责小样本分割介绍等内容的撰写。
FENG Yuan is a postgraduate student of School of Computer Science and Engineering, South China University of Techno-logy. Her research interests include few-shot image classifi-cation, zero-shot image classification, zero-shot semantic segmentation.
In this paper, she is responsible for the parts of the introduction of few-shot segmentation. E-mail: 202120143952@mail.scut.edu.cn
基金资助:
广州市重点研发计划项目(202103010005);广东省国际合作项目(2021A0505030017);国家自然科学基金(62176095)

A Survey on Few-Shot Image Semantic Segmentation

CHEN Qiong(),YANG Yong(),HUANG Tianlin(),FENG Yuan

School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China

Received:2021-11-11 Online:2021-12-20 Published:2022-01-26
Contact: CHEN Qiong

摘要/Abstract

摘要：

【目的】对小样本语义分割方法进行系统而全面的介绍,为后续小样本分割算法设计工作提供参考。【方法】当前的小样本分割方法借助基于度量的元学习方法来完成少样本情况下的语义分割任务。根据度量工具是否可学习,将小样本分割算法分为基于参数结构和基于原型结构的小样本分割算法,简述了两类算法的优缺点。【结果】对该领域的一些经典工作和近年来的工作做了具体的分析,并给出了小样本分割算法的主要应用场景。【结论】在此基础上,分析了小样本分割存在的关键问题和挑战,对小样本分割未来的发展方向和趋势进行了讨论。

关键词: 计算机视觉, 图像数据, 深度学习, 图像分割, 小样本学习, 小样本语义分割

Abstract:

[Objective] This paper introduces the few-shot image semantic segmentation methods systematically and comprehensively, as a reference to the design of the few-shot segmentation algorithm.[Methods] The metric-based meta-learning method is employed to perform the few-shot segmentation tasks. According to whether the metric tool is learnable, the few-shot segmentation algorithms are divided into prototype-based methods and parameter-based methods. This paper describes the advantages and disadvantages of both algorithms. [R-esults]Some classical and recent research works about the few-shot image semantic segmentation are analyzed in detail, together with an introduction of the main applications of the few-shot segmentation algorithm. [Conclusions] Hereinafter, the future development direction and trend of few-shot image segmentation are discussed, and its key problems and challenges are analyzed.

Key words: computer vision, image data, deep learning, image segmentation, few-shot learning, few-shot segmentation

陈琼,杨咏,黄天林,冯媛. 小样本图像语义分割综述[J]. 数据与计算发展前沿, 2021, 3(6): 17-34.

CHEN Qiong,YANG Yong,HUANG Tianlin,FENG Yuan. A Survey on Few-Shot Image Semantic Segmentation[J]. Frontiers of Data and Computing, 2021, 3(6): 17-34.

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模型	方法	骨干网络	Mean-IoU (1-shot)	FB-IoU (1-shot)	Mean-IoU (5-shot)	FB-IoU (5-shot)
PANet^[54] SG-One^[53] FWB^[28] PPNet^[31] SimPropNet^[56] SAGNN^[42] MLC^[58] ASGNet^[30]	原型原型原型原型原型原型原型原型	VGG-16 VGG-16 ResNet-101 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50	48.1 46.3 56.2 51.5 57.2 62.1 62.1 59.3	68.5 63.1 - - 73.0 73.2 69.2	55.7 47.1 59.9 62.0 60.0 62.8 66.1 63.9	70.7 65.9 - - 72.9 73.3 74.2
CANet^[32] PFENet^[33] BriNet^[34] PMMS^[26] PGNet^[48] CRNet^[36] SCL_PFENet^[41] SCL_CANet^[41] CMN^[50] CWT^[51]	参数参数参数参数参数参数参数参数参数参数	ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50	55.4 60.8 57.1 55.2 56.0 55.7 61.8 57.5 62.8 56.4	66.2 73.3 69.9 66.8 71.9 70.3 72.3	57.1 61.9 56.8 58.5 58.8 62.9 59.2 63.7 63.7	69.6 73.9 70.5 71.5 72.8 70.7 72.8

模型	方法	骨干网络	Mean-IoU (1-shot)	FB-IoU (1-shot)	Mean-IoU (5-shot)	FB-IoU (5-shot)
PANet^[54] FWB^[28] PPNet^[31] SAGNN^[42] MLC^[58] ASGNet^[30]	原型原型原型原型原型原型	VGG-16 ResNet-101 ResNet-50 ResNet-101 ResNet-50 ResNet-50	20.9 21.2 29.0 37.2 33.9 34.6	59.2 60.9 60.4	29.7 23.7 38.5 42.9 40.6 42.5	63.5 63.4 67.0
CANet^[32] PFENet^[33] BriNet^[34] PMMS^[26] SCL_PFENet^[41] CMN^[50] CWT^[51]	参数参数参数参数参数参数参数	ResNet-50 ResNet-101 ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50	32.4 34.4 29.6 37.0 39.3 32.9	58.6 61.7	37.4 34.3 39.9 43.1 41.3	61.9 63.3

模型	方法	骨干网络	参数量
PANet^[54] SG-One^[53] FWB^[28] PPNet^[31]	原型原型原型原型	VGG-16 VGG-16 ResNet-101 ResNet-50	14.7M 19.0M 43.0M 31.5M
CANet^[32] PFENet^[33] BriNet^[34] PMMS^[26] PGNet^[48]	参数参数参数参数参数	ResNet-50 ResNet-50 ResNet-50 ResNet-50 ResNet-50	19.0M 10.8M 20.5M 19.6M 17.2M

小样本图像语义分割综述

A Survey on Few-Shot Image Semantic Segmentation

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