基于判别语义表示学习的零概率分类

Meng Ye, Yuhong Guo
{"title":"基于判别语义表示学习的零概率分类","authors":"Meng Ye, Yuhong Guo","doi":"10.1109/CVPR.2017.542","DOIUrl":null,"url":null,"abstract":"Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have disjoint label spaces, has become increasingly popular in the computer vision community. In this paper, we propose a novel zero-shot learning method based on discriminative sparse non-negative matrix factorization. The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors. To fully exploit the aligned semantic information contained in the learned representation vectors of the instances, we develop a label propagation based testing procedure to classify the unlabeled instances from the unseen classes in the target domain. We conduct experiments on four standard zero-shot learning image datasets, by comparing the proposed approach to the state-of-the-art zero-shot learning methods. The empirical results demonstrate the efficacy of the proposed approach.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"55 1","pages":"5103-5111"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"119","resultStr":"{\"title\":\"Zero-Shot Classification with Discriminative Semantic Representation Learning\",\"authors\":\"Meng Ye, Yuhong Guo\",\"doi\":\"10.1109/CVPR.2017.542\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have disjoint label spaces, has become increasingly popular in the computer vision community. In this paper, we propose a novel zero-shot learning method based on discriminative sparse non-negative matrix factorization. The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors. To fully exploit the aligned semantic information contained in the learned representation vectors of the instances, we develop a label propagation based testing procedure to classify the unlabeled instances from the unseen classes in the target domain. We conduct experiments on four standard zero-shot learning image datasets, by comparing the proposed approach to the state-of-the-art zero-shot learning methods. The empirical results demonstrate the efficacy of the proposed approach.\",\"PeriodicalId\":6631,\"journal\":{\"name\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)\",\"volume\":\"55 1\",\"pages\":\"5103-5111\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"119\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPR.2017.542\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.2017.542","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 119

摘要

零射击学习是一种无监督域自适应的特殊情况,源域和目标域具有不相交的标签空间,在计算机视觉界越来越受欢迎。本文提出了一种基于判别稀疏非负矩阵分解的零次学习方法。该方法旨在通过非负稀疏矩阵分解来识别跨两个域的一组常见的高级语义组件,同时强制该公共组件空间中的图像表示向量与基于属性的标签表示向量判别对齐。为了充分利用实例的学习表示向量中包含的对齐语义信息,我们开发了一种基于标签传播的测试过程,将目标域中未标记的实例与未见过的类进行分类。我们在四个标准的零射击学习图像数据集上进行了实验,并将所提出的方法与最先进的零射击学习方法进行了比较。实证结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Zero-Shot Classification with Discriminative Semantic Representation Learning
Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have disjoint label spaces, has become increasingly popular in the computer vision community. In this paper, we propose a novel zero-shot learning method based on discriminative sparse non-negative matrix factorization. The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors. To fully exploit the aligned semantic information contained in the learned representation vectors of the instances, we develop a label propagation based testing procedure to classify the unlabeled instances from the unseen classes in the target domain. We conduct experiments on four standard zero-shot learning image datasets, by comparing the proposed approach to the state-of-the-art zero-shot learning methods. The empirical results demonstrate the efficacy of the proposed approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
FFTLasso: Large-Scale LASSO in the Fourier Domain Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces Joint Gap Detection and Inpainting of Line Drawings Wetness and Color from a Single Multispectral Image
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1