返回
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics

Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics

ID：40720067

大小：1.97 MB

页数：10页

时间：2019-08-06

Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics _第1页
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics _第2页
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics _第3页
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics _第4页
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics _第5页
资源描述：

《Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics 》由会员上传分享，免费在线阅读，更多相关内容在学术论文-天天文库。

1、LearningImageEmbeddingsusingConvolutionalNeuralNetworksforImprovedMulti-ModalSemanticsDouweKiela∗LeonBottou´UniversityofCambridgeMicrosoftResearchComputerLaboratoryNewYorkdouwe.kiela@cl.cam.ac.ukleon@bottou.orgAbstractlabeleddataset(Krizhevskyetal.,2012).Theconvolutionallayersarethenusedasmid-

2、levelWeconstructmulti-modalconceptrepre-featureextractorsonavarietyofcomputervi-sentationsbyconcatenatingaskip-gramsiontasks(Oquabetal.,2014;Girshicketal.,linguisticrepresentationvectorwithavi-2013;ZeilerandFergus,2013;Donahueetal.,sualconceptrepresentationvectorcom-2014).Althoughtransferringc

3、onvolutionalnet-putedusingthefeatureextractionlayersworkfeaturesisnotanewidea(DriancourtandofadeepconvolutionalneuralnetworkBottou,1990),thesimultaneousavailabilityof(CNN)trainedonalargelabeledobjectlargedatasetsandcheapGPUco-processorshasrecognitiondataset.Thistransferlearn-contributedtotheac

4、hievementofconsiderableingapproachbringsaclearperformanceperformancegainsonavarietycomputervisiongainoverfeaturesbasedonthetraditionalbenchmarks:“SIFTandHOGdescriptorspro-bag-of-visual-wordapproach.Experimen-ducedbigperformancegainsadecadeago,andtalresultsarereportedontheWordSim353nowdeepconvo

5、lutionalfeaturesareprovidingaandMENsemanticrelatednessevaluationsimilarbreakthrough”(Razavianetal.,2014).tasks.Weusevisualfeaturescomputedus-ThisworkreportsonresultsobtainedbyusingingeitherImageNetorESPGameimages.CNN-extractedfeaturesinmulti-modalsemanticrepresentationmodels.Theseresultsareint

6、erest-1Introductioninginseveralrespects.First,thesesuperiorfea-Recentworkshaveshownthatmulti-modalse-turesprovidetheopportunitytoincreasetheper-manticrepresentationmodelsoutperformuni-formancegapachievedbyaugmentinglinguisticmodallinguisticmodelsonavarietyoftasks,in-featureswithmulti-modalfeat

7、ures.Second,thiscludingmodelingsemanticrelatednessandpre-increasedperformanceconfirmsthatthemulti-dictingcompositionality(FengandLapata,2010;modalperformanceimprovementresultsfromtheLeongandMihalcea,2011;Brunietal.,2012;informationcontai

当前文档最多预览五页，下载文档查看全文

此文档下载收益归作者所有

当前文档最多预览五页，下载文档查看全文
温馨提示：
1. 部分包含数学公式或PPT动画的文件，查看预览时可能会显示错乱或异常，文件下载后无此问题，请放心下载。
2. 本文档由用户上传，版权归属用户，天天文库负责整理代发布。如果您对本文档版权有争议请及时联系客服。
3. 下载前请仔细阅读文档内容，确认文档内容符合您的需求后进行下载，若出现内容与标题不符可向本站投诉处理。
4. 下载文档时可能由于网络波动等原因无法下载或下载错误，付费完成后未能成功下载的用户请联系客服处理。