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. 多模态人工智能系统全国重点实验室
  5. 多媒体计算与图形学团队
Deep Relative Attributes

文献类型：期刊论文

作者Yang, Xiaoshan1; Zhang, Tianzhu1; Xu, Changsheng1; Yan, Shuicheng2; Hossain, M. Shamim3; Ghoneim, Ahmed3,4
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2016-09-01
卷号18期号:9页码:1832-1842
关键词Deep Learning Relative Attributes (Ra)
DOI10.1109/TMM.2016.2582379
文献子类Article
英文摘要Relative attribute (RA) learning aims to learn the ranking function describing the relative strength of the attribute. Most of current learning approaches learn a linear ranking function for each attribute by use of the hand-crafted visual features. Different from the existing study, in this paper, we propose a novel deep relative attributes (DRA) algorithm to learn visual features and the effective nonlinear ranking function to describe the RA of image pairs in a unified framework. Here, visual features and the ranking function are learned jointly, and they can benefit each other. The proposed DRA model is comprised of five convolutional neural layers, five fully connected layers, and a relative loss function which contains the contrastive constraint and the similar constraint corresponding to the ordered image pairs and the unordered image pairs, respectively. To train the DRA model effectively, we make use of the transferred knowledge from the large scale visual recognition on ImageNet [1] to the RA learning task. We evaluate the proposed DRA model on three widely used datasets. Extensive experimental results demonstrate that the proposed DRA model consistently and significantly outperforms the state-of-the-art RA learning methods. On the public OSR, PubFig, and Shoes datasets, compared with the previous RA learning results [2], the average ranking accuracies have been significantly improved by about 8%, 9%, and 14%, respectively.
WOS关键词COMMUNITY-CONTRIBUTED PHOTOS ; OBJECT CLASSES ; RETRIEVAL
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000381437800013
资助机构Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia(RGP-229)
源URL[http://ir.ia.ac.cn/handle/173211/12644]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位1.National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
3.King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia
4.Menoufia Univ, Coll Sci, Dept Comp Sci, Menoufia 32721, Egypt
推荐引用方式
GB/T 7714		 Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Deep Relative Attributes[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2016,18(9):1832-1842.
APA Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,Yan, Shuicheng,Hossain, M. Shamim,&Ghoneim, Ahmed.(2016).Deep Relative Attributes.IEEE TRANSACTIONS ON MULTIMEDIA,18(9),1832-1842.
MLA Yang, Xiaoshan,et al."Deep Relative Attributes".IEEE TRANSACTIONS ON MULTIMEDIA 18.9(2016):1832-1842.

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。