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《cvpr18-Wing Loss for Robust Facial Landmark Localisation With Convolutional Neural Networks》由會員上傳分享，免費在線閱讀，更多相關內(nèi)容在學術論文-天天文庫。
1、WingLossforRobustFacialLandmarkLocalisationwithConvolutionalNeuralNetworksZhen-HuaFeng1JosefKittler1MuhammadAwais1PatrikHuber1Xiao-JunWu21CentreforVision,SpeechandSignalProcessing,UniversityofSurrey,GuildfordGU27XH,UK2SchoolofIoTEngineering,JiangnanUni
2、versity,Wuxi214122,China{z.feng,j.kittler,m.a.rana}@surrey.ac.uk,patrikhuber@gmail.com,wuxiaojun@jiangnan.edu.cnAbstract4040w=5,=0.5w=10,=0.5Wepresentanewlossfunction,namelyWingloss,forro-35w=5,=135w=10,=1w=5,=2w=10,=2bustfaciallandmarklocalisationwith
3、ConvolutionalNeu-30w=5,=330w=10,=3w=5,=4w=10,=4ralNetworks(CNNs).We?rstcompareandanalysedif-2525ferentlossfunctionsincludingL2,L1andsmoothL1.The2020analysisoftheselossfunctionssuggeststhat,forthetrain-1515ingofaCNN-basedlocalisationmodel,moreattention1
4、010shouldbepaidtosmallandmediumrangeerrors.Tothisend,wedesignapiece-wiselossfunction.Thenewloss55ampli?estheimpactoferrorsfromtheinterval(-w,w)by00-20-1001020-20-1001020switchingfromL1losstoamodi?edlogarithmfunction.(a)w=5(b)w=10Toaddresstheproblemofun
5、der-representationofsam-Figure1.OurWinglossfunction(Eq.5)plottedwithdifferentpleswithlargeout-of-planeheadrotationsinthetrainingparametersettings,wherewlimitstherangeofthenon-linearpartset,weproposeasimplebuteffectiveboostingstrategy,re-and?controlsthe
6、curvature.Bydesign,weamplifytheimpactofferredtoaspose-baseddatabalancing.Inparticular,wethesampleswithsmallandmediumrangeerrorstothenetworkdealwiththedataimbalanceproblembyduplicatingthetraining.minoritytrainingsamplesandperturbingthembyinject-ingrando
7、mimagerotation,boundingboxtranslationandotherdataaugmentationapproaches.Last,theproposedveryaccuratefaciallandmarklocalisationinconstrainedapproachisextendedtocreateatwo-stageframeworkforscenarios,evenusingtraditionalapproachessuchasAc-robustfacialland
8、marklocalisation.Theexperimentalre-tiveShapeModel(ASM)[7],ActiveAppearanceModelsultsobtainedonAFLWand300Wdemonstratethemerits(AAM)[8]andConstrainedLocalModel(CLM)[11].TheoftheWinglossfunction,andprovethesuperiorityoftheexistingchallenge
