Ø w-block(S-SVM):
Ø λ-block(loss-augmentedparametricenergyminimization):
LearningtoCombineMid-levelCuesforObjectProposalGenerationTomLee,Sanja Fidler,SvenDickinson
Ø AnovelParametricMin-Loss(PML)structuredlearningframeworkforparametricenergyfunctions.
Ø PMLlearnstopredictmultipleoutputsusinganovellossfunction.
Ø PMLbridgesthegapbetweenlearningandinferenceforparametricenergyfunctions.
Ø PMLisapplicabletoanydomainthatusesparametricenergyfunctions.
Contributions
Ø Objectproposalsreduceanexhaustivesetofhypothesestoafewplausiblecandidatesegments.
Ø Objectproposalsareoftenpredictionsfromparametricenergyfunctions (CPMC[2]etc.)
Ø Parametricenergyfunctionscanencoderelevantbottom-upgroupingcues[4].
Ø Butnopreviousapproachexistsforlearningtopredictmultipleoutputswithparametricenergyfunctions.
Motivation
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Segment Overlap by #Proposals, VOC’12 Vall
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τ=50
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oursCPMCSelective Search
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Segment Overlap by #Proposals, COCO’14 Val
oursMulticueSuperpixel ClosureMCG
Results
Ø WeachieveresultscomparablewithCPMC[2]andMCG[1]Ø Weoutperformmethodsthatlacklearning,e.g.SelectiveSearch[5]
Ø BiasenergytodifferentlocationsØ Maximumsuperpixel distance
Location- andcolor-baseddiversification Postprocessing
Ø Discardnon-maximumproposalsamongproposalswithhighoverlap.
Ø TrainSVMondeepfeaturestoassignanobjectnessscoretoeachproposal.Ø Biasenergytodifferentforeground-backgroundcolorpairs
Ø Gaussianmixturemodelofsuperpixel colors
Ø Theappearancecuediscouragesdivisionofsimilarcolorsandtextures:
Ø Theclosurecuediscouragesgapsalongboundaries:
Ø Thesymmetrycuediscouragesdivisionofsymmetricparts:
Ø Theenergyisnormalizedbyareabyafactorλ:
Ø Evaluatemultiplepredictedsegmentsagainstonecorrectgroundtruthsegment.
Ø Lossfunctionideallyexpressesa“min”:
Ø Innerlossfunctionmeasurestheerrorofasinglepredictedsegment:
Ø Upperboundforinnerlossfunction(hingeloss):
Ø Upperboundforlossfunction(min-hingeloss[3]):
Ø Regularizedtrainingobjective:Ø Nonnegativeweightsandnonnegative
λ coefficientsguaranteeasmallsetofsolutionsfromparametricmaxflow.
Ø Onepredictionforaspecificλ:
Ø Asetofpredictionsoverarangeofλ:
Parametricenergyfunction
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[1]Arbelaez etal.,CVPR2014. [4]Leeetal.,ACCV2014.[2]Carreira &Sminchisescu,PAMI2012. [5]Uijlings etal.,IJCV2013.[3]Guzman-Riveraetal.,NIPS2012.
Diversification
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