howell36790

MobileNetV2Inverted Residuals and Linear Bottlenecks

Abstract Inthispaperwedescribeanewmobilearchitecture, MobileNetV2,thatimprovesthestateoftheartperfor- manceofmobilemodelsonmultipletasksandbench- mark

深度学习最新中文版pdf

深度学习书籍2017年9月的最新高清pdf版，beta版第一章引言11.1本书面向的读者..........................101.2深度学习的历史趋势.......................111.2.1神经网络的众多名称和命运变迁...........121.2.2与日俱增

FineGrainedHeadPoseEstimationWithoutKeypoints

AbstractEstimatingtheheadposeofapersonisacrucialprob-lemthathasalargeamountofapplicationssuchasaidingingazeestimation,modelingattention,fitting3Dmodel

cascade r_cnn paper

Inobjectdetection,anintersectionoverunion(IoU)thresholdisrequiredtodefinepositivesandnegatives.Anobjectdetector,trainedwithlowIoUthreshold,e.g.0.5,usu

ShuffleNet V2Practical Guidelines for Efficient CNN Architecture Design

Abstract.Currently,theneuralnetworkarchitecturedesignismostlyguidedbytheindirectmetricofcomputationcomplexity,i.e.,FLOPs.However,thedirectmetric,e.g.,

DSFD DualShotFaceDetector

AbstractRecently,ConvolutionalNeuralNetwork(CNN)hasachievedgreatsuccessinfacedetection.However,itre-mainsachallengingproblemforthecurrentfacedetection

AnExtremelyEfficientConvolutionalNeuralNetworkforMobileDevices

Abstract We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very l

FocalLossforDenseObjectDetection

Abstract The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a spar