中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 中科院工业视觉智能装备工程实验室
  5. 精密感知与控制
AGUnet: Annotation-guided U-net for fast one-shot video object segmentation

文献类型：期刊论文

作者Yin, Yingjie1,2,3; Xu, De2,3; Wang, Xingang2,3; Zhang, Lei1
刊名PATTERN RECOGNITION
出版日期2021-02-01
卷号110页码:10
关键词Fully-convolutional Siamese network U-net Interactive image segmentation Video object segmentation
ISSN号0031-3203
DOI10.1016/j.patcog.2020.107580
通讯作者Yin, Yingjie(yingjie.yin@ia.ac.cn)
英文摘要The problem of semi-supervised video object segmentation has been popularly tackled by fine-tuning a general-purpose segmentation deep network on the annotated frame using hundreds of iterations of gra-dient descent. The time-consuming fine-tuning process, however, makes these methods difficult to use in practical applications. We propose a novel architecture called Annotation Guided U-net (AGUnet) for fast one-shot video object segmentation (VOS). AGUnet can quickly adapt a model trained on static images to segmenting the given target in a video by only several iterations of gradient descent. Our AGUnet is inspired by interactive image segmentation, where the interested target is segmented by using user annotated foreground. However, in AGUnet we use a fully-convolutional Siamese network to automatically annotate the foreground and background regions and fuse such annotation information into the skip connection of a U-net for VOS. Our AGUnet can be trained end-to-end effectively on static images instead of video sequences as required by many previous methods. The experiments show that AGUnet runs much faster than current state-of-the-art one-shot VOS algorithms while achieving competitive accuracy, and it has high generalization capability. (c) 2020 Elsevier Ltd. All rights reserved.
资助项目National Natural Science Foundation of China[61703398] ; National Natural Science Foundation of China[61672446] ; National Natural Science Foundation of China[61873266] ; Hong Kong Scholars Program[XJ2017031]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000585302200002
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Hong Kong Scholars Program
源URL[http://ir.ia.ac.cn/handle/173211/41647]  
专题精密感知与控制研究中心_精密感知与控制
通讯作者Yin, Yingjie
作者单位1.Hong Kong Polytech Univ, Dept Comp, Hung Hom, Kowloon, Hong Kong, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Yingjie,Xu, De,Wang, Xingang,et al. AGUnet: Annotation-guided U-net for fast one-shot video object segmentation[J]. PATTERN RECOGNITION,2021,110:10.
APA Yin, Yingjie,Xu, De,Wang, Xingang,&Zhang, Lei.(2021).AGUnet: Annotation-guided U-net for fast one-shot video object segmentation.PATTERN RECOGNITION,110,10.
MLA Yin, Yingjie,et al."AGUnet: Annotation-guided U-net for fast one-shot video object segmentation".PATTERN RECOGNITION 110(2021):10.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。