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CNN Attention Networks

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Taeoh Kim

1) The document discusses different types of attention mechanisms in CNNs including self-attention and simplified attention for recalibration. 2) It reviews the evolution of CNN architectures including AlexNet, VGG, ResNet and variants, DenseNet, ResNeXt, Xception, MobileNet and ShuffleNet. 3) These attention mechanisms and CNN architectures are applied to tasks like image recognition, machine translation and image captioning.

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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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