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1、Instance-sensitiveFullyConvolutionalNetworksJifengDai1,KaimingHe1,YiLi2?,ShaoqingRen3?,JianSun112MicrosoftResearch,TsinghuaUniversity,3UniversityofScienceandTechnologyofChinaAbstractFullyconvolutionalnetworks(FCNs)havebeenprovenverysuccessfulforsemanticsegmentation,buttheFCNoutputsare
2、unawareofobjectinstances.Inthispaper,wedevelopFCNsthatarecapableofproposinginstance-levelsegmentcandidates.IncontrasttothepreviousFCNthatgeneratesonescoremap,ourFCNisdesignedtocomputeasmallsetofinstance-sensitivescoremaps,eachofwhichistheoutcomeofapixel-wiseclassifierofarelativepositio
3、ntoinstances.Ontopoftheseinstance-sensitivescoremaps,asimpleassemblingmoduleisabletooutputinstancecandidateateachposition.IncontrasttotherecentDeepMaskmethodforsegmentinginstances,ourmethoddoesnothaveanyhigh-dimensionallayerrelatedtothemaskresolution,butinsteadexploitsimagelocalcohere
4、nceforestimatinginstances.Wepresentcom-petitiveresultsofinstancesegmentproposalonbothPASCALVOCandMSCOCO.1IntroductionFullyconvolutionalnetworks(FCN)[1]havebeenprovenaneffectiveend-to-endsolutiontosemanticimagesegmentation.AnFCNproducesascoremapofasizeproportionaltotheinputimage,whereev
5、erypixelrepresentsaclassifierofobjects.Despitegoodaccuracyandeaseofusage,FCNsarenotdirectlyapplicableforproducinginstancesegments(Fig.1(top)).Previousinstancesemanticsegmentationmethods(e.g.,[2,3,4,5])ingeneralresortedtooff-the-shelfsegmentproposalmethods(e.g.,[6,7]).Inthispaper,wedevel
6、opanend-to-endfullyconvolutionalnetworkthatisarXiv:1603.08678v1[cs.CV]29Mar2016capableofsegmentingcandidateinstances.LiketheFCNin[1],inourmethodeverypixelstillrepresentsaclassifier;butunlikeanFCNthatgeneratesonescoremap(foroneobjectcategory),ourmethodcomputesasetofinstance-sensitivesco
7、remaps,whereeachpixelisaclassifierofrelativepositionstoanobjectinstance(Fig.1(bottom)).Forexample,witha3×3regulargriddepictingrelativepositions,weproduceasetof9scoremapsinwhich,e.g.,themap#6inFig.1hashighscoresonthe“rightside”ofobjectinstances.Withthissetofscoremaps,weareabletogenerate
8、anobj
