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

doi:10.1109/JAS.2023.123207

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
	Wenqi Ren; Yang Tang; Qiyu Sun; Chaoqiang Zhao; Qing-Long Han
刊名	IEEE/CAA Journal of Automatica Sinica
	2024
卷号	11 期号:5 页码:1106-1126
关键词	Computer vision deep learning few-shot learning low-shot learning semantic segmentation zero-shot learning
ISSN号	2329-9266
DOI	10.1109/JAS.2023.123207
英文摘要	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 large-scale 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/zero-shot 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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55702]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Wenqi Ren,Yang Tang,Qiyu Sun,et al. Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(5):1106-1126.
APA	Wenqi Ren,Yang Tang,Qiyu Sun,Chaoqiang Zhao,&Qing-Long Han.(2024).Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview.IEEE/CAA Journal of Automatica Sinica,11(5),1106-1126.
MLA	Wenqi Ren,et al."Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview".IEEE/CAA Journal of Automatica Sinica 11.5(2024):1106-1126.