文献类型：期刊文献

中文题名：Visual Semantic Segmentation Based on Few/Zero-Shot Learning:An Overview

作者：Wenqi Ren[1];Yang Tang[1];Qiyu Sun[1];Chaoqiang Zhao[2];Qing-Long Han[3]

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China;[2]National Key Laboratory of Air-Based Information Perception and Fusion,Aviation Industry Corporation of China,Luoyang 471000,China;[3]School of Science,Computing and Engineering Technologies,Swinburne University of Technology,Melbourne VIC 3122,Australia

年份：2024

卷号：11

期号：5

起止页码：1106

中文期刊名：IEEE/CAA Journal of Automatica Sinica

外文期刊名：自动化学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；

基金：supported by National Key Research and Development Program of China(2021YFB1714300);the National Natural Science Foundation of China(62233005);in part by the CNPC Innovation Fund(2021D002-0902);Fundamental Research Funds for the Central Universities and Shanghai AI Lab;sponsored by Shanghai Gaofeng and Gaoyuan Project for University Academic Program Development。

语种：英文

中文关键词：Visual;segmentation;separating;

摘要：Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.