BayesianSegNet:ModelUncertaintyinDeepConvolutionalEncoder-DecoderArchitecturesforSceneUnderstandingAlexKendall,VijayBadrinarayananandRobertoCipolla
http://mi.eng.cam.ac.uk/projects/segnet/
References1. VijayBadrinarayanan,AlexKendall,RobertoCipolla.SegNet:ADeepConvolutionalEncoder-DecoderArchitectureforImageSegmentation. PAMI,2017.2. AlexKendall,VijayBadrinarayananandRobertoCipolla.BayesianSegNet:ModelUncertaintyinDeepConvolutionalEncoder-DecoderArchitecturesfor
SceneUnderstanding.BMVC,2017.3. AlexKendallandYarinGal.WhatUncertaintiesDoWeNeedinBayesianDeepLearningforComputerVision?arXivpreprintarXiv:1703.04977,2017.4. AlexKendall,YarinGalandRobertoCipolla.Multi-TaskLearningUsingUncertaintytoWeighLossesforSceneGeometryandSemantics.arXivpreprint
arXiv:1705.07115,2017.
Insights• Wecanobtainper-classmodeluncertaintyestimatesforsceneunderstandingmodels• Bayesianinferencemoreimportantinlateencoderandearlydecoderlayers• Improvessegmentationperformanceby2-3% acrosspopularmodels• MCdropoutoutperformsweightaveragingafter6samplesandconvergesafter40samples• Especiallyeffectiveforsmalldatasets(e.g.CamVid)• Modeluncertaintyincreasesforrareanddifficultclasses• Modeluncertaintyisusefulforsafeautonomousdecisionmaking,activelearningandlabelpropagation
FurtherApplications• DistinguishAleatoric (sensor)
uncertaintyandEpistemic (model)uncertainty[3]
• Useuncertaintytoimprovemulti-tasklearning[4]
• Semanticsegmentation,instancesegmentationanddepth regressionfromasingleinputimage[4]
Model Standard Bayesian
DilationNet 71.3% 73.1%
FCN 62.2% 65.4%
SegNet 59.1% 60.5%
Convolutional Encoder-DecoderInputSegmentation
Model Uncertainty
Stochastic DropoutSamples
Conv + Batch Normalisation + ReLUDropout Pooling/Upsampling Softmax
mean
variance
RGB Image
WeuseMonteCarlodropoutsamplingattesttimetogenerateaposteriordistributionofpixelclasslabels.
BayesianSegNetarchitecturePASCALVOC2012
TestServerPerformance
InputImageGroundTruthModelSegmentationAleatoricUncertaintyEpistemicUncertainty