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CNN Attention Networks

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Taeoh Kim

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CNNAttention-basedNetworks + Attention,CNNReview Tensorflow-KR PR-163,TaeohKim MVPLAB,YonseiUniv
Contents PR-163: CNN_Attention_Networks • Attention, Self-Attention in NLP • CNN-Review • CNN Attention Networks for Recognition • CNN Attention Networks for Other Vision Tasks
Review 1: Attention
Neural Networks PR-163: CNN_Attention_Networks Input Feature Hidden State Representation Prediction
Attention PR-163: CNN_Attention_Networks Quary{Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} {Key, Value} 𝑦 = ෍ 𝑖 𝑤𝑖 𝑥𝑖 𝑦 = ෍ 𝑖 𝑓 𝑄𝑢𝑎𝑟𝑦, 𝐾𝑒𝑦 × 𝑉𝑎𝑙𝑢𝑒 Output
Fully Connected Neural Network PR-163: CNN_Attention_Networks Input Feature Hidden State Representation 𝑤1 𝑤2 … 𝑤 𝑛 Fully Connected NN Represent Blue using Weighted Sum of Inputs without Constraint
Convolutional Neural Network PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (V) (V) (V) (V) (V) (V) 𝑤1 𝑤2 𝑤3 Convolutional NN Represent Blue using Weighted Sum of Inputs from Current Position Q
Attention PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 Attention Represent Blue using Weighted Sum of Inputs from Blue State and Inputs 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐵𝑆, 𝐾 𝑛
ex) Machine Translation (PR-055) PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐵𝑆, 𝐾 𝑛 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐾𝑜𝑟𝑒𝑎𝑛1 𝐾𝑜𝑟𝑒𝑎𝑛2
ex) Image Captioning PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐵𝑆, 𝐾 𝑛 𝐼𝑚𝑎𝑔𝑒 𝐹𝑒𝑎𝑡𝑢𝑟𝑒 𝐶𝑎𝑝𝑡𝑖𝑜𝑛1 𝐶𝑎𝑝𝑡𝑖𝑜𝑛2
Self-Attention PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 Self-Attention Represent Blue using Weighted Sum of Inputs from Input and Inputs 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛
ex) Transformer (Enc) (PR-049, PR-161) PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐹𝑒𝑎𝑡𝑢𝑟𝑒
ex) Transformer (Dec) PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛 𝐾𝑜𝑟𝑒𝑎𝑛1:𝑛 𝐸𝑛𝑔𝑙𝑖𝑠ℎ 𝐹𝑒𝑎𝑡𝑢𝑟𝑒 (K, V) 𝐾𝑜𝑟𝑒𝑎𝑛 𝑛+1 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐸 𝑛
CNN Self-Attention for Image = Representation PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤1 𝑤2 … 𝑤 𝑛 𝑤 𝑛 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑓 𝐾2, 𝐾 𝑛 𝐼𝑚𝑎𝑔𝑒 𝑅𝑒𝑐𝑜𝑔𝑛𝑖𝑡𝑖𝑜𝑛
ex) Non-local Neural Networks (CVPR18, PR-083) PR-163: CNN_Attention_Networks 𝑦𝑖 = 1 σ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗)) ෍ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗))𝑊𝑔 𝑥𝑗 Reshape 1x1Conv Operation Soft max HxWxC HWxC HWxC CxHW HWxHW HWxC Quary Key Value HxWxC + HxWxC
CNN Simplified-Attention for Image = Recalibration PR-163: CNN_Attention_Networks Input Feature Hidden State Representation (K, V) (K, V) (K, V) (K, V) (K, V) (K, V) 𝑤2 𝑤2 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑓 𝐾2, 𝐾 𝑛 𝐼𝑚𝑎𝑔𝑒 𝑅𝑒𝑐𝑜𝑔𝑛𝑖𝑡𝑖𝑜𝑛
CNN Simplified-Attention for Image = Recalibration PR-163: CNN_Attention_Networks x
ex) Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks
Summary PR-163: CNN_Attention_Networks Attention Quary Structure Objective Examples Attention Current States Recurrent Representation NMT, Captioning, VQA Self-Attention Input Itself Feed-Forward Representation Transformer Non-local NN Input Itself Feed-Forward Recalibration SE-Net, RAN, CBAM
Review 2: CNN Networks
CNN Review PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 WideResNet 2016.05 / 1063 SE-Net 2017.09 / 724 Residual Attention Net 2017.04 / 257 Non-local Network 2017.11 / 240 CBAM 2018.07 / 54 GCNet 2019.04 / - ResNetv2 2016.03 / 1926 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417
CNN Review PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 WideResNet 2016.05 / 1063 SE-Net 2017.09 / 724 Residual Attention Net 2017.04 / 257 Non-local Network 2017.11 / 240 CBAM 2018.07 / 54 GCNet 2019.04 / - ResNetv2 2016.03 / 1926 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417 𝑇𝑜𝑑𝑎𝑦
Plain Networks PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 • PlainNetworksusingMax-Pooling • LowPerformance/LargeParameters,Operations ILSVRC12 ILSVRC14
ResNet PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 • DeeperNetworksusingSkip-Connection Res Block Res Block Res Block Res Block ILSVRC15
ResNet ResBlock PR-163: CNN_Attention_Networks Res Block Res Block Res Block Res Block 1x1 3x3 1x1 c c/4 c/4 c BatchNorm ReLU xN
ResNet Variants PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 WideResNet 2016.05 / 1063 ResNetv2 2016.03 / 1926 • Pre-activationResNet 1x1 3x3 1x1 c c/4 c/4 c
ResNet Variants PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 WideResNet 2016.05 / 1063 ResNetv2 2016.03 / 1926 • WiderChannelResNet 3x3 3x3 c c x k c
ResNet with Cardinality PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 • Cardinality =GroupConv • PR-034:Xception • PR-044:MobileNet • PR-054:SuffleNet/ResNeXt • ModifyConvolutionOperators
ResNeXt (C=32) PR-163: CNN_Attention_Networks 256 256 1x1 1x1 1x1 3x3 3x3 3x3 1x1 1x1 1x1 4 4 256 + 32 Blocks ...... ...... ......
PR-163: CNN_Attention_Networks 256 256 3x3 3x3 3x3 128 4 4 1x1 1x1 C O N C A T G R O U P C O N V 128 ResNeXt (C=32) with Group Conv.
PR-163: CNN_Attention_Networks 256 256 1x1 1x1 1x1 3x3 3x3 3x3 1x1 1x1 1x1 4 4 256 + 32 Blocks X (1x1x4)x32 Summation ...... ...... ...... ResNeXt (C=32)
PR-163: CNN_Attention_Networks 256 256 3x3 3x3 3x3 128 4 4 1x1 1x1 C O N C A T G R O U P C O N V 128 ... ...X 1x1x128Summation ...... ...... ResNeXt (C=32) with Group Conv.
Xception (G=Channel) PR-163: CNN_Attention_Networks 256 256 3x3 3x3 3x3 128 1 1 1x1 1x1 C O N C A T G R O U P C O N V 128 ...... ......
MobileNet / ShuffleNet PR-163: CNN_Attention_Networks • MobileNet:LightweightXception • Xception: 1x1→3x3Depthwise • MobileNet: 3x3Depthwise→1x1 • ShuffleNet:LightweightResNeXt+ChannelShuffle
DenseNet PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 • DenseNet:ConcatPreviousLayers:PR-028 Dense Block Dense Block Dense Block Dense Block
DenseBlock PR-163: CNN_Attention_Networks Dense Block Dense Block Dense Block Dense Block 1x1 3x3 Conv Conv Conv ConvConv
Inception / NASNet PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417 • EngineeredNetworks • PR-034:Inception • PR-069:NASNet ILSVRC14
CNN Performances PR-163: CNN_Attention_Networks https://github.com/CeLuigi/models-comparison.pytorch
CNN Review PR-163: CNN_Attention_Networks Category Networks Pros Cons Plain AlexNet, VGG Simple Good Transfer Low Performance ResNet ResNet Simple Cardinality ResNeXt/Xception MobileNet/ShuffleNet Cost Efficient + Performance Group Conv DenseNet DenseNet Cost Efficient + Performance Memory I/O Engineering Inception NASNet SoTA Complex
CNN Review PR-163: CNN_Attention_Networks Category Networks Pros Cons Plain AlexNet, VGG Simple Good Transfer Low Performance ResNet ResNet Simple Cardinality ResNeXt/Xception MobileNet/ShuffleNet Cost Efficient + Performance Group Conv DenseNet DenseNet Cost Efficient + Performance Memory I/O Engineering Inception NASNet SoTA Complex Attention Module SENet, CBAM, GCNet Simple + Performance
CNN Review PR-163: CNN_Attention_Networks
CNN x Attention
CNN Attention-Networks PR-163: CNN_Attention_Networks AlexNet 2012 / 39646 VGG 2014.09 / 22554 GoogleNet 2014.09 / 13233 ResNet 2015.12 / 21871 DenseNet 2016.08 / 3591 ResNeXt 2016.11 / 913 Xception 2016.10 / 847 MobileNet 2017.04 / 1553 ShuffleNet 2017.07 / 407 WideResNet 2016.05 / 1063 SE-Net 2017.09 / 724 Residual Attention Net 2017.04 / 257 Non-local Network 2017.11 / 240 CBAM 2018.07 / 54 GCNet 2019.04 / - ResNetv2 2016.03 / 1926 Inception-2,3 2015.12 / 3752 Inception-4 Inception-ResNet 2016.02 / 2152 NASNet 2017.07. / 417 ILSVRC17
Spatial Transformer Networks (NIPS15, PR-011) PR-163: CNN_Attention_Networks • Recalibration(withTransform)
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks c + Conv(1x1) Conv(3x3) Conv(1x1) • OriginalResNet(BottleNeckBlock)
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks c + Conv(1x1) Conv(3x3) Conv(1x1) Mask Generation Networks Sigmoid ReLU x • Recalibrate{Spatial+Channel) • ActuallyNotResidual
Residual Attention Network (CVPR17) PR-163: CNN_Attention_Networks • Results:InterpretableFeatures
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks c c + x c cc/16 1x1 1x1 AvgPool Conv(1x1) Conv(3x3) Conv(1x1) Sigmoid ReLU BN • RecalibrateChannelOnly
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks c c + x c cc/16 1x1 1x1 AvgPool Conv(1x1) Conv(3x3) Conv(1x1) Sigmoid ReLU BN • RecalibrateChannelOnly Per-Channel GlobalSpatialContext (Squeeze)
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks c c + x c cc/16 1x1 1x1 AvgPool Conv(1x1) Conv(3x3) Conv(1x1) Sigmoid ReLU BN • RecalibrateChannelOnly ModelingInter-Channel Relationship(Excitation)
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks
Squeeze-and-Excitation Networks (CVPR18) PR-163: CNN_Attention_Networks • ChannelRecalibrationStats.
Bottleneck Attention Networks (BMVC18) PR-163: CNN_Attention_Networks c + Conv(1x1) Conv(3x3) Conv(1x1) 1x1 1x1 AvgPool + x + Sigmoid ReLU BN 3x3 Dilated Conv 1x1 r r 3x3 Channel Attention Spatial Attention
Convolutional Block Attention Networks (ECCV18) PR-163: CNN_Attention_Networks c + x c cc/16 AvgPool MaxPool x 7x7 Channel-wise AvgPool MaxPool c Conv(1x1) Conv(3x3) Conv(1x1) + Share 1x1 Sigmoid ReLU BN Channel Attention Spatial Attention
BAM / CBAM Results PR-163: CNN_Attention_Networks
RAN / SE / BAM / CBAM Comparison PR-163: CNN_Attention_Networks Network Module Position Attention RAN (CVPR17) Modified ChannelxSpatial 3D SE (CVPR18) In the ResBlock Channel BAM (BMVC18) Before the Stride=2 ResBlock Channel, Spatial Parallel CBAM (ECCV18) In the ResBlock Channel, Spatial Sequential
Non-local Networks PR-163: CNN_Attention_Networks • RepresentSpatial-Only Soft maxHxWxC HxWxC HWxC HWxC CxHW HWxHW HWxC Quary Key Value HxWxC +
Global-Context Attention Networks PR-163: CNN_Attention_Networks Softmax HxWxC HxWxC HWxC 1xHW C Key Value HxWxC + +
Global-Context Attention Networks PR-163: CNN_Attention_Networks Softmax HxWxC HxWxC HWxC 1xHW C Key Value HxWxC + + SpatialRepresentation
Global-Context Attention Networks PR-163: CNN_Attention_Networks Softmax HxWxC HxWxC HWxC 1xHW C Key Value HxWxC + + SpatialRepresentation ChannelAttention
Global-Context Attention Networks PR-163: CNN_Attention_Networks QuaryIndependentRepresentation→Recalibration
Global-Context Attention Networks PR-163: CNN_Attention_Networks NLNet SimplifiedNLNet SENet GCNet SpatialWeightedSum PerPixel (HxW) SpatialWeightedSum Shared (Scalar) SpatialAggregation (GlobalAvgPool) →forChannelRecalibration SpatialWeightedSum →forChannelRecalibration
Global-Context Attention Networks PR-163: CNN_Attention_Networks QuaryIndependentRepresentation→Recalibration Non-localNetworksMeetSqueeze-ExcitationNetworksandBeyond(GCNet)
Global-Context Attention Networks PR-163: CNN_Attention_Networks
Summary PR-163: CNN_Attention_Networks Network Attention Spatial Modeling RAN (CVPR17) ChannelxSpatial 3D Network SE (CVPR18) Channel Avg Pool BAM (BMVC18) Channel, Spatial Parallel Avg Pool CBAM (ECCV18) Channel, Spatial Sequential Avg Pool + Max Pool NLNet (CVPR18) Spatial (Representation) Non-local Representation GCNet (Preprint19) Channel Non-local Representation
CNN x Attention: Other Vision Tasks
Self-Attention GAN PR-163: CNN_Attention_Networks
Style Transfer (CVPR19) PR-163: CNN_Attention_Networks ArbitraryStyleTransferwithStyle-AttentionalNetworks
PSANet (ECCV18) / Context Encoding (CVPR18) / OCNet (2018) PR-163: CNN_Attention_Networks
Dual Attention Network (CVPR19) PR-163: CNN_Attention_Networks
Criss-Cross Non-local Attention Networks (2019) PR-163: CNN_Attention_Networks
Semantic Segmentation PR-163: CNN_Attention_Networks Network Performance (Cityscape) mIoU Structure DenseASPP (CVPR18) 80.6 DenseNet PSANet (ECCV18) 80.1 Spatial Attention Context Encoding (CVPR18) - Channel Attention CCNet (Arxiv19) 81.4 Fast NL-Net DANet (CVPR19) 81.5 NL-Net (Spatial + Channel) OCNet (Arxiv18) 81.7 NL-Net + PSP
Non-local in SISR PR-163: CNN_Attention_Networks
Single Image Super-Resolution PR-163: CNN_Attention_Networks Network Performance (set5, PSNR) Structure RDN (CVPR18) 38.24 / 32.47 (x2, x4) DenseNet RNRN (ICLR19) 38.17 / 32.49 NL-Net RCAN (ECCV18) 38.27 / 32.63 Channel Attention SAN (CVPR19) 38.31 / 32.64 Channel Attention + NL-Net
Conclusion PR-163: CNN_Attention_Networks • Attention (Recurrent) vs Self-Attention (Feed-Forward) • Representation vs Recalibration • Channel Attention: Simple • Spatial Attention: Global Information
ThankYou Q&A?

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