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1、LearningtoCompareImagePatchesviaConvolutionalNeuralNetworksSergeyZagoruykoNikosKomodakisUniversiteParisEst,EcoledesPontsParisTechUniversiteParisEst,EcoledesPontsParisTechsergey.zagoruyko@imagine.enpc.frnikos.komodakis@enpc.frAbstractsimilarityInthispaperweshowhowtolearndirectlyfromimagedata(i.e.
2、,withoutresortingtomanually-designedfeatures)decisionnetworkageneralsimilarityfunctionforcomparingimagepatches,whichisataskoffundamentalimportanceformanycom-ConvNetputervisionproblems.Toencodesuchafunction,weoptforaCNN-basedmodelthatistrainedtoaccountforawidevarietyofchangesinimageappearance.Tot
3、hatend,weexploreandstudymultipleneuralnetworkarchitectures,whicharespeci?callyadaptedtothistask.Weshowthatsuchanapproachcansigni?cantlyoutperformthestate-of-patch1patch2the-artonseveralproblemsandbenchmarkdatasets.Figure1.Ourgoalistolearnageneralsimilarityfunctionforim-agepatches.Toencodesuchafu
4、nction,herewemakeuseofand1.Introductionexploreconvolutionalneuralnetworkarchitectures.Comparingpatchesacrossimagesisprobablyoneofthesoftware)largedatasetsthatcontainpatchcorrespondencesmostfundamentaltasksincomputervisionandimageanal-betweenimages[22].Thisbegsthefollowingquestion:canysis.Itisoft
5、enusedasasubroutinethatplaysanimportantwemakeproperuseofsuchdatasetstoautomaticallylearnroleinawidevarietyofvisiontasks.Thesecanrangefromasimilarityfunctionforimagepatches?low-leveltaskssuchasstructurefrommotion,widebaselineThegoalofthispaperistoaf?rmativelyaddressthematching,buildingpanoramas,a
6、ndimagesuper-resolution,abovequestion.Ouraimisthustobeabletogenerateauptohigher-leveltaskssuchasobjectrecognition,imagepatchsimilarityfunctionfromscratch,i.e.,withoutattempt-retrieval,andclassi?cationofobjectcategories,tomentioningtouseanymanuallydesignedfeaturesbutinsteaddi-afewcharacteristicex
7、amples.rectlylearnthisfunctionfromannotatedpairsofrawimageOfcourse,theproblemofdecidingiftwopatchescorre-patches.Tothatend,inspiredalsobytherecentadvancesinspondtoeachotherornotisquitechallengingasthereexistneuralarchitectur