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...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.展开更多
Information technology education has played a more important role under the background of“Internet+”.However,a combination of education and information technology is only limited between online teaching platforms an...Information technology education has played a more important role under the background of“Internet+”.However,a combination of education and information technology is only limited between online teaching platforms and massive open online courses(MOOC).This paper proposes a visual teaching system based on cloud computing and big data techniques via combing virtual and real techniques online and offline to provide rich teaching resources for students.It can also use the digital human-computer interaction answering function to address students’questions.Additionally,it can provide a medium for young teachers to quickly improve their professional teaching skills.This paper aims to achieve a multimedia system via integrating“Internet+”technology with education to help improve talent training and abilities of young teachers.展开更多
基金supported by National Key Research and Development Program of China(2021YFB1714300)the National Natural Science Foundation of China(62233005)+2 种基金in part by the CNPC Innovation Fund(2021D002-0902)Fundamental Research Funds for the Central Universities and Shanghai AI Labsponsored by Shanghai Gaofeng and Gaoyuan Project for University Academic Program Development。
文摘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.
基金supported in part by the Ideological and Political Education of Financial Decision Support System under KVSZZZ202315in part by Collaborative Education by the Ministry of Education under 220501210164954in part by Teaching Education Reform of NPU under 06410-23GZ230106。
文摘Information technology education has played a more important role under the background of“Internet+”.However,a combination of education and information technology is only limited between online teaching platforms and massive open online courses(MOOC).This paper proposes a visual teaching system based on cloud computing and big data techniques via combing virtual and real techniques online and offline to provide rich teaching resources for students.It can also use the digital human-computer interaction answering function to address students’questions.Additionally,it can provide a medium for young teachers to quickly improve their professional teaching skills.This paper aims to achieve a multimedia system via integrating“Internet+”technology with education to help improve talent training and abilities of young teachers.