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1、UnifyingIdentificationandContextLearningforPersonRecognitionQingqiuHuang,YuXiong,DahuaLinCUHK-SenseTimeJointLab,TheChineseUniversityofHongKong{hq016,xy017,dhlin}@ie.cuhk.edu.hkVisualContextSocialContextAbstractEvent-PersonHeadDespitethegreatsuccessoffacer
2、ecognitiontechniques,FacerecognizingpersonsunderunconstrainedsettingsremainsFaceGBodyPerson-Personchallenging.Issueslikeprofileviews,unfavorablelighting,andocclusionscancausesubstantialdifficulties.PreviousUpperBodyInferenceworkshaveattemptedtotacklethispr
3、oblembyexploitingInferenceInferenceLearningthecontext,e.g.clothesandsocialrelations.Whileshowing???KateLeonardoKatepromisingimprovement,theyareusuallylimitedintwoim-portantaspects,relyingonsimpleheuristicstocombinedif-ferentcuesandseparatingtheconstructi
4、onofcontextfrompeopleidentities.Inthiswork,weaimtomovebeyondsuchlimitationsandproposeanewframeworktoleveragecon-textforpersonrecognition.Inparticular,weproposeaRe-Figure1:Personrecognitionunderunconstrainedsettingsre-gionAttentionNetwork,whichislearnedto
5、adaptivelycom-mainsaverychallengingproblem.Inferencepurelybyfacerecog-binevisualcueswithinstance-dependentweights.Wealsonitiontechniqueswouldfailinmanycases.Weproposeaframe-developaunifiedformulation,wherethesocialcontextsareworktotacklethisproblem,whichc
6、ombinesvisualcontextwithlearnedalongwiththereasoningofpeopleidentities.Theseadaptiveweightsandunifiespersonrecognitionwithsocialcon-modelssubstantiallyimprovetherobustnesswhenworkingtextlearning.withthecomplexcontextualrelationsinunconstraineden-vironment
7、s.Ontwolargedatasets,PIPA[27]andCastInThedifficultiesaboveareessentiallyduetothefactthatMovies(CIM),anewdatasetproposedinthiswork,ourfacialappearanceishighlysensitivetoenvironmentalcon-methodconsistentlyachievesstate-of-the-artperformanceditions.Totacklet
8、hisproblem,anaturalideaistoleverageundermultipleevaluationpolicies.anotherimportantsourceofinformation,namelythecon-text.Itisourcommonexperiencethatwecaneasilyrec-ognizeafamiliarpersonbylookingatthewearing,thesur-1.Introdu
