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Wepresentaconceptuallysimple,flexible,andgeneralframeworkforobjectinstancesegmentation.Ourapproachefficientlydetectsobjectsinanimagewhilesimultaneouslygeneratingahigh-qualitysegmentationmaskforeachinstance.Themethod,calledMaskR-CNN,extendsFasterR-CNNbyaddingabranchforpredictinganobjectmaskinparallelwiththeexistingbranchforbounding…
ThispaperproposesaFastRegion-basedConvolutionalNetworkmethod(FastR-CNN)forobjectdetection.FastR-CNNbuildsonpreviousworktoefficientlyclassifyobjectproposalsusingdeepconvolutionalnetworks.Comparedtopreviouswork,FastR-CNNemploysseveralinnovationstoimprovetrainingandtestingspeedwhilealsoincreasingdetectionaccuracy.FastR-CNNtrainstheverydeep...
Inobjectdetection,anintersectionoverunion(IoU)thresholdisrequiredtodefinepositivesandnegatives.Anobjectdetector,trainedwithlowIoUthreshold,e.g.0.5,usuallyproducesnoisydetections.However,detectionperformancetendstodegradewithincreasingtheIoUthresholds.Twomainfactorsareresponsibleforthis:1)overfittingduringtraining,duetoexponentially...
Inthispaper,wefirstinvestigatewhytypicaltwo-stagemethodsarenotasfastassingle-stage,fastdetectorslikeYOLOandSSD.WefindthatFasterR-CNNandR-FCNperformanintensivecomputationafterorbeforeRoIwarping.FasterR-CNNinvolvestwofullyconnectedlayersforRoIrecognition,whileR-FCNproducesalargescoremaps.Thus,thespeedofthesenetworksisslow…
Objectdetectionperformance,asmeasuredonthecanonicalPASCALVOCdataset,hasplateauedinthelastfewyears.Thebest-performingmethodsarecomplexensemblesystemsthattypicallycombinemultiplelow-levelimagefeatureswithhigh-levelcontext.Inthispaper,weproposeasimpleandscalabledetectionalgorithmthatimprovesmeanaverageprecision(mAP)bymorethan…
Wepresentaconceptuallysimple,flexible,andgeneralframeworkforobjectinstancesegmentation.Ourapproachefficientlydetectsobjectsinanimagewhilesimultaneouslygeneratingahigh-qualitysegmentationmaskforeachinstance.Themethod,calledMaskR-CNN,extendsFasterR-CNNbyaddingabranchforpredictinganobjectmaskinparallelwiththeexistingbranchforbounding…
ThispaperproposesaFastRegion-basedConvolutionalNetworkmethod(FastR-CNN)forobjectdetection.FastR-CNNbuildsonpreviousworktoefficientlyclassifyobjectproposalsusingdeepconvolutionalnetworks.Comparedtopreviouswork,FastR-CNNemploysseveralinnovationstoimprovetrainingandtestingspeedwhilealsoincreasingdetectionaccuracy.FastR-CNNtrainstheverydeep...
Inobjectdetection,anintersectionoverunion(IoU)thresholdisrequiredtodefinepositivesandnegatives.Anobjectdetector,trainedwithlowIoUthreshold,e.g.0.5,usuallyproducesnoisydetections.However,detectionperformancetendstodegradewithincreasingtheIoUthresholds.Twomainfactorsareresponsibleforthis:1)overfittingduringtraining,duetoexponentially...
Inthispaper,wefirstinvestigatewhytypicaltwo-stagemethodsarenotasfastassingle-stage,fastdetectorslikeYOLOandSSD.WefindthatFasterR-CNNandR-FCNperformanintensivecomputationafterorbeforeRoIwarping.FasterR-CNNinvolvestwofullyconnectedlayersforRoIrecognition,whileR-FCNproducesalargescoremaps.Thus,thespeedofthesenetworksisslow…
Objectdetectionperformance,asmeasuredonthecanonicalPASCALVOCdataset,hasplateauedinthelastfewyears.Thebest-performingmethodsarecomplexensemblesystemsthattypicallycombinemultiplelow-levelimagefeatureswithhigh-levelcontext.Inthispaper,weproposeasimpleandscalabledetectionalgorithmthatimprovesmeanaverageprecision(mAP)bymorethan…
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