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Pr083 Non-local Neural Networks

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Taeoh Kim

Tensorflow Korea 논문읽기 모임 PR12의 83번째 발표는 Non-local Neural Networks입니다.

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PR 083 (29th April, 2018) Taeoh Kim
• CVPR18 Poster • Inspired by Non-local Means
Slides fromBIL717ImageProcessing 2012
Slides fromBIL717ImageProcessing 2012
Slides fromBIL717ImageProcessing 2012
Slides fromBIL717ImageProcessing 2012
• Average Similar Pixels • Do not Average non-Similar Pixels Problem) Not Enough Similar Pixels in LOCAL REGIONS
• Average Similar Pixels • Do not Average non-Similar Pixels Problem) Not Enough Similar Pixels in LOCAL REGIONS  Get More Samples in Non-LOCAL REGIONS
Slides fromBIL717ImageProcessing 2012
Slides fromBIL717ImageProcessing 2012
Slides fromBIL717ImageProcessing 2012
Slides fromBIL717ImageProcessing 2012 𝑁𝐿𝑀𝐹 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑉𝑝 − 𝑉𝑞 2 𝐼 𝑞 𝐵𝐴 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐼 𝑞 𝐺 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑝 − 𝑞 2 𝐼 𝑞 𝐺 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑝 − 𝑞 2 𝐺 𝜎 𝑟 𝐼 𝑝 − 𝐼 𝑞 1 𝐼 𝑞
𝑁𝐿𝑀𝐹 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑉𝑝 − 𝑉𝑞 2 𝐼 𝑞 Output Value Representation (ProbabilityDistribution) TargetValue (Pixel) vs AllValues(Pixel) Inputs
PR-049 , Attention isAllYou Need
𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛 𝑄, 𝐾, 𝑉 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑄𝐾 𝑇 𝑑 𝑘 𝑉 Output Value Representation (ProbabilityDistribution) TargetValue (Query) vs AllValues(Keys) Inputs PR-049 , Attention isAllYou Need
𝑀𝑢𝑙𝑡𝑖𝐻𝑒𝑎𝑑 𝑄, 𝐾, 𝑉 = 𝑊 ∙ 𝐶𝑜𝑛𝑐𝑎𝑡 ℎ𝑒𝑎𝑑1, … , ℎ𝑒𝑎𝑑ℎ ℎ𝑒𝑎𝑑𝑖 = 𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑄𝑊𝑖𝑄, 𝐾𝑊𝑖𝐾, 𝑉𝑊𝑖𝑉) PR-049 , Attention isAllYou Need
𝑁𝐿𝑀𝐹 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑉𝑝 − 𝑉𝑞 2 𝐼 𝑞 Output Value Representation (ProbabilityDistribution) TargetValue (Pixel) vs AllValues(Pixel) Inputs
𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗) Output Value Representation (ProbabilityDistribution) TargetValue (Pixel) vs AllValues(Pixel) Inputs
Another Representation of Input Local Pixels = Weighted Sum of Local Pixels with Learned Filter 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗∈3×3 𝑤𝑗 𝑔(𝑥𝑗)
Another Representation of Non-Local Pixels = Weighted Sum of All Pixels with Similarity 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗)
Another Representation of Non-Local Pixels = Weighted Sum of All Pixels with Similarity +Learning… 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗)
𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗) • Gaussian 𝑓 𝑥𝑖, 𝑥𝑗 = exp(𝑥𝑖 𝑇 ∙ 𝑥𝑗) • Embedded Gaussian 𝑓 𝑥𝑖, 𝑥𝑗 = exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗)) • Dot Product 𝑓 𝑥𝑖, 𝑥𝑗 = 𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗) • Concatenation 𝑓 𝑥𝑖, 𝑥𝑗 = 𝑅𝑒𝐿𝑈(𝑤𝑓 𝑇 𝜃(𝑥𝑖) ∙ 𝜙(𝑥𝑗) )
𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗) 𝑔 𝑥𝑗 = 𝑊𝑔 𝑥𝑗 For Feature Extraction
𝑦𝑖 = 1 σ 𝑗 exp(𝑥𝑖 𝑇 ∙ 𝑥𝑗) ෍ 𝑗 exp(𝑥𝑖 𝑇 ∙ 𝑥𝑗) 𝑊𝑔 𝑥𝑗 Soft max HxWx1024 HxWx512 HWx512 HWx1024 1024xHW HWxHW HWx512 HxWx512 Reshape 1x1Conv Operation
𝑦𝑖 = 1 σ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗)) ෍ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗))𝑊𝑔 𝑥𝑗 Soft max HxWx1024 HxWx512 HWx512 HWx512 512xHW HWxHW HWx512 HxWx512 Reshape 1x1Conv Operation
𝑦𝑖 = 1 𝑁 ෍ 𝑗 𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗) 𝑊𝑔 𝑥𝑗 1/N HxWx1024 HxWx512 HWx512 HWx512 512xHW HWxHW HWx512 HxWx512 Reshape 1x1Conv Operation
𝑦𝑖 = 1 𝑁 ෍ 𝑗 𝑅𝑒𝐿𝑈(𝑤𝑓 𝑇 𝜃(𝑥𝑖) ∙ 𝜙(𝑥𝑗) )𝑊𝑔 𝑥𝑗 1/N HxWx1024 HxWx512 HWx512 HWx512 HWx512 HWx1024 HWx512 HxWx512 Reshape 1x1Conv Operation 1024 xHW +ReLU HWxHW
HxWx1024 HxWx512 NL Operation HxWx1024 1x1Conv + Residual 𝑧𝑖 = 𝑊𝑧 𝑦𝑖 + 𝑥𝑖
Another Representation of Non-Local Pixels = Weighted Sum of All Pixels with Similarity + Learning? 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗)
Recalibrate Features? • Global Representation • Global Context • Long-range Dependencies • Shorter Paths
Attention isAllYouNeed,2017
Squeeze –and–Excitation Networks,CVPR 2018 Channel-wise Feature Recalibration - SENet (ILSVRC 2017 Winner) • 2 FC (Fully Connected) Layers Between Channels • Excitation Layer Output (Representation) x Input = Output
• Squeeze-and-Excitation Networks (Channel-wise) 𝑥𝑖 / Learned Weights / 𝑥𝑗 • Self-Attention (Spatial) Embedded 𝑥𝑖 / Similarity Weights / Embedded 𝑥𝑗 / Positional Encoding • Non-local Neural Networks (Spatial/Temporal) Embedded or Not 𝑥𝑖 / Similarity Weights / Embedded 𝑥𝑗
Layer Operation Repeat Output Size Conv1 7x7, 64, s=2 64x112x112 Pool1 3x3, s=2 64x56x56 Res2 [1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x56x56 Res3 [1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x28x28 Res4 [1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x14x14 Res5 [1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x7x7 Pool2 7x7 2048 FC # of Category
3x224x224 64x112x112 64x56x56 7 x 7 P o o l 1 x 1 3 x 3 1 x 1 1 x 1 + Stride=2 Conv Stride=1 ConvStride=2 Pool 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 256x56x56 64 6464256 256 6464256 256 6464256 256
Stride=2 Conv Stride=1 ConvStride=2 Pool 256x56x56 512x28x28 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 128128512 512
Stride=2 Conv Stride=1 ConvStride=2 Pool 512x28x28 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 256256 1024 1024 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1024x14x14
Stride=2 Conv Stride=1 ConvStride=2 Pool 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1024x14x14 2048x7x7 512 512 2048 2048 2048x1x1 1000 7 x 7 F C
Layer Operation Repeat Output Size Conv1 7x7, 64, s=2 64x112x112 Pool1 3x3, s=2 64x56x56 Res2 [1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x56x56 Res3 [1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x28x28 Res4 [1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x14x14 Res5 [1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x7x7 Pool2 7x7 2048 FC # of Category
Layer Operation Repeat Output Size Conv1 7x7, 64, s=2 64x16x112x112 Pool1 3x3x3, s=2,2,2 64x8x56x56 Res2 [1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x8x56x56 Pool_T 3x1x1, s=2,1,1 256x4x56x56 Res3 [1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x4x28x28 Res4 [1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x4x14x14 Res5 [1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x4x7x7 Pool2 4x7x7 2048x1 FC # of Category
Layer Operation Repeat Output Size Conv1 5x7x7, 64, s=2 64x16x112x112 Pool1 3x3x3, s=2,2,2 64x8x56x56 Res2 [1x1, 64 / 3x3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x8x56x56 Pool_T 3x1x1, s=2,1,1 256x4x56x56 Res3 [1x1, 128, s=2 / 3x3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x4x28x28 Res4 [1x1, 256, s=2 / 3x3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x4x14x14 Res5 [1x1, 512, s=2 / 3x3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x4x7x7 Pool2 4x7x7 2048x1 FC # of Category
Layer Operation Repeat Output Size Conv1 5x7x7, 64, s=2 64x16x112x112 Pool1 3x3x3, s=2,2,2 64x8x56x56 Res2 [3x1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x8x56x56 Pool_T 3x1x1, s=2,1,1 256x4x56x56 Res3 [3x1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x4x28x28 Res4 [3x1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x4x14x14 Res5 [3x1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x4x7x7 Pool2 4x7x7 2048x1 FC # of Category
Pretrained 3x3 Copy x3 Devide Weights by1/3 Pretrained 1x1 Copy x3 Devide Weights by1/3 3x3x3 3x1x1 • 2D Conv Training  3D Conv Test
• Kinetics Dataset • ~246k Videos (Train) • 20k Videos (Validation) • 400 Human Action Categories
• Add 1 Non-local Block • Right before the last residual block of res4 • The Attentional Behavior is Not the Key to the Improvement • Similarity + Learning >> Similarity (Gaussian)
Stride=2 Conv Stride=1 ConvStride=2 Pool 512x28x28 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 256256 1024 1024 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1024x14x14
• Similar • Except res5 (Feature Map size is too Small)
• Long-range Multi-hop Communication • Shallow 5-block ResNet50 > Deep baseline ResNet101 • Add NL Blocks > Add Residual Blocks
• Add 5 NL Blocks • Spacetime >> Space = Time >> Baseline
• NL C2D > I3D (3D ConvNet Baseline) • Smaller Number of FLOPS
• Add 5 NL Blocks
• 128 Frame (vs 32 Frame)
Pr083 Non-local Neural Networks
Pr083 Non-local Neural Networks
Pr083 Non-local Neural Networks

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Pr083 Non-local Neural Networks

  • 1. PR 083 (29th April, 2018) Taeoh Kim
  • 2. • CVPR18 Poster • Inspired by Non-local Means
  • 3.
  • 4.
  • 9. • Average Similar Pixels • Do not Average non-Similar Pixels Problem) Not Enough Similar Pixels in LOCAL REGIONS
  • 10. • Average Similar Pixels • Do not Average non-Similar Pixels Problem) Not Enough Similar Pixels in LOCAL REGIONS  Get More Samples in Non-LOCAL REGIONS
  • 14. Slides fromBIL717ImageProcessing 2012 𝑁𝐿𝑀𝐹 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑉𝑝 − 𝑉𝑞 2 𝐼 𝑞 𝐵𝐴 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐼 𝑞 𝐺 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑝 − 𝑞 2 𝐼 𝑞 𝐺 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑝 − 𝑞 2 𝐺 𝜎 𝑟 𝐼 𝑝 − 𝐼 𝑞 1 𝐼 𝑞
  • 15. 𝑁𝐿𝑀𝐹 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑉𝑝 − 𝑉𝑞 2 𝐼 𝑞 Output Value Representation (ProbabilityDistribution) TargetValue (Pixel) vs AllValues(Pixel) Inputs
  • 16.
  • 17. PR-049 , Attention isAllYou Need
  • 18. 𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛 𝑄, 𝐾, 𝑉 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑄𝐾 𝑇 𝑑 𝑘 𝑉 Output Value Representation (ProbabilityDistribution) TargetValue (Query) vs AllValues(Keys) Inputs PR-049 , Attention isAllYou Need
  • 19. 𝑀𝑢𝑙𝑡𝑖𝐻𝑒𝑎𝑑 𝑄, 𝐾, 𝑉 = 𝑊 ∙ 𝐶𝑜𝑛𝑐𝑎𝑡 ℎ𝑒𝑎𝑑1, … , ℎ𝑒𝑎𝑑ℎ ℎ𝑒𝑎𝑑𝑖 = 𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛(𝑄𝑊𝑖𝑄, 𝐾𝑊𝑖𝐾, 𝑉𝑊𝑖𝑉) PR-049 , Attention isAllYou Need
  • 20.
  • 21. 𝑁𝐿𝑀𝐹 𝐼 𝑝 = 1 𝑊 ෍ 𝑞 𝐺 𝜎 𝑉𝑝 − 𝑉𝑞 2 𝐼 𝑞 Output Value Representation (ProbabilityDistribution) TargetValue (Pixel) vs AllValues(Pixel) Inputs
  • 22. 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗) Output Value Representation (ProbabilityDistribution) TargetValue (Pixel) vs AllValues(Pixel) Inputs
  • 23. Another Representation of Input Local Pixels = Weighted Sum of Local Pixels with Learned Filter 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗∈3×3 𝑤𝑗 𝑔(𝑥𝑗)
  • 24. Another Representation of Non-Local Pixels = Weighted Sum of All Pixels with Similarity 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗)
  • 25. Another Representation of Non-Local Pixels = Weighted Sum of All Pixels with Similarity +Learning… 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗)
  • 26. 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗) • Gaussian 𝑓 𝑥𝑖, 𝑥𝑗 = exp(𝑥𝑖 𝑇 ∙ 𝑥𝑗) • Embedded Gaussian 𝑓 𝑥𝑖, 𝑥𝑗 = exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗)) • Dot Product 𝑓 𝑥𝑖, 𝑥𝑗 = 𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗) • Concatenation 𝑓 𝑥𝑖, 𝑥𝑗 = 𝑅𝑒𝐿𝑈(𝑤𝑓 𝑇 𝜃(𝑥𝑖) ∙ 𝜙(𝑥𝑗) )
  • 27. 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗) 𝑔 𝑥𝑗 = 𝑊𝑔 𝑥𝑗 For Feature Extraction
  • 28. 𝑦𝑖 = 1 σ 𝑗 exp(𝑥𝑖 𝑇 ∙ 𝑥𝑗) ෍ 𝑗 exp(𝑥𝑖 𝑇 ∙ 𝑥𝑗) 𝑊𝑔 𝑥𝑗 Soft max HxWx1024 HxWx512 HWx512 HWx1024 1024xHW HWxHW HWx512 HxWx512 Reshape 1x1Conv Operation
  • 29. 𝑦𝑖 = 1 σ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗)) ෍ 𝑗 exp(𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗))𝑊𝑔 𝑥𝑗 Soft max HxWx1024 HxWx512 HWx512 HWx512 512xHW HWxHW HWx512 HxWx512 Reshape 1x1Conv Operation
  • 30. 𝑦𝑖 = 1 𝑁 ෍ 𝑗 𝜃(𝑥𝑖 𝑇 ) ∙ 𝜙(𝑥𝑗) 𝑊𝑔 𝑥𝑗 1/N HxWx1024 HxWx512 HWx512 HWx512 512xHW HWxHW HWx512 HxWx512 Reshape 1x1Conv Operation
  • 31. 𝑦𝑖 = 1 𝑁 ෍ 𝑗 𝑅𝑒𝐿𝑈(𝑤𝑓 𝑇 𝜃(𝑥𝑖) ∙ 𝜙(𝑥𝑗) )𝑊𝑔 𝑥𝑗 1/N HxWx1024 HxWx512 HWx512 HWx512 HWx512 HWx1024 HWx512 HxWx512 Reshape 1x1Conv Operation 1024 xHW +ReLU HWxHW
  • 33.
  • 34. Another Representation of Non-Local Pixels = Weighted Sum of All Pixels with Similarity + Learning? 𝑦𝑖 = 1 𝐶(𝑥) ෍ 𝑗 𝑓 𝑥𝑖, 𝑥𝑗 𝑔(𝑥𝑗)
  • 35. Recalibrate Features? • Global Representation • Global Context • Long-range Dependencies • Shorter Paths
  • 37. Squeeze –and–Excitation Networks,CVPR 2018 Channel-wise Feature Recalibration - SENet (ILSVRC 2017 Winner) • 2 FC (Fully Connected) Layers Between Channels • Excitation Layer Output (Representation) x Input = Output
  • 38. • Squeeze-and-Excitation Networks (Channel-wise) 𝑥𝑖 / Learned Weights / 𝑥𝑗 • Self-Attention (Spatial) Embedded 𝑥𝑖 / Similarity Weights / Embedded 𝑥𝑗 / Positional Encoding • Non-local Neural Networks (Spatial/Temporal) Embedded or Not 𝑥𝑖 / Similarity Weights / Embedded 𝑥𝑗
  • 39.
  • 40. Layer Operation Repeat Output Size Conv1 7x7, 64, s=2 64x112x112 Pool1 3x3, s=2 64x56x56 Res2 [1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x56x56 Res3 [1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x28x28 Res4 [1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x14x14 Res5 [1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x7x7 Pool2 7x7 2048 FC # of Category
  • 41. 3x224x224 64x112x112 64x56x56 7 x 7 P o o l 1 x 1 3 x 3 1 x 1 1 x 1 + Stride=2 Conv Stride=1 ConvStride=2 Pool 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 256x56x56 64 6464256 256 6464256 256 6464256 256
  • 42. Stride=2 Conv Stride=1 ConvStride=2 Pool 256x56x56 512x28x28 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 128128512 512
  • 43. Stride=2 Conv Stride=1 ConvStride=2 Pool 512x28x28 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 256256 1024 1024 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1024x14x14
  • 44. Stride=2 Conv Stride=1 ConvStride=2 Pool 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1024x14x14 2048x7x7 512 512 2048 2048 2048x1x1 1000 7 x 7 F C
  • 45. Layer Operation Repeat Output Size Conv1 7x7, 64, s=2 64x112x112 Pool1 3x3, s=2 64x56x56 Res2 [1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x56x56 Res3 [1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x28x28 Res4 [1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x14x14 Res5 [1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x7x7 Pool2 7x7 2048 FC # of Category
  • 46. Layer Operation Repeat Output Size Conv1 7x7, 64, s=2 64x16x112x112 Pool1 3x3x3, s=2,2,2 64x8x56x56 Res2 [1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x8x56x56 Pool_T 3x1x1, s=2,1,1 256x4x56x56 Res3 [1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x4x28x28 Res4 [1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x4x14x14 Res5 [1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x4x7x7 Pool2 4x7x7 2048x1 FC # of Category
  • 47. Layer Operation Repeat Output Size Conv1 5x7x7, 64, s=2 64x16x112x112 Pool1 3x3x3, s=2,2,2 64x8x56x56 Res2 [1x1, 64 / 3x3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x8x56x56 Pool_T 3x1x1, s=2,1,1 256x4x56x56 Res3 [1x1, 128, s=2 / 3x3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x4x28x28 Res4 [1x1, 256, s=2 / 3x3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x4x14x14 Res5 [1x1, 512, s=2 / 3x3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x4x7x7 Pool2 4x7x7 2048x1 FC # of Category
  • 48. Layer Operation Repeat Output Size Conv1 5x7x7, 64, s=2 64x16x112x112 Pool1 3x3x3, s=2,2,2 64x8x56x56 Res2 [3x1x1, 64 / 3x3, 64 / 1x1, 256] + [1x1, 256] x3 256x8x56x56 Pool_T 3x1x1, s=2,1,1 256x4x56x56 Res3 [3x1x1, 128, s=2 / 3x3, 128 / 1x1, 512] + [1x1, 512] x4 512x4x28x28 Res4 [3x1x1, 256, s=2 / 3x3, 256 / 1x1, 1024] + [1x1, 1024] x6 1024x4x14x14 Res5 [3x1x1, 512, s=2 / 3x3, 512 / 1x1, 2048] + [1x1, 2048] x3 2048x4x7x7 Pool2 4x7x7 2048x1 FC # of Category
  • 49. Pretrained 3x3 Copy x3 Devide Weights by1/3 Pretrained 1x1 Copy x3 Devide Weights by1/3 3x3x3 3x1x1 • 2D Conv Training  3D Conv Test
  • 50.
  • 51. • Kinetics Dataset • ~246k Videos (Train) • 20k Videos (Validation) • 400 Human Action Categories
  • 52. • Add 1 Non-local Block • Right before the last residual block of res4 • The Attentional Behavior is Not the Key to the Improvement • Similarity + Learning >> Similarity (Gaussian)
  • 53. Stride=2 Conv Stride=1 ConvStride=2 Pool 512x28x28 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 256256 1024 1024 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1 x 1 3 x 3 1 x 1 1 x 1 + 1024x14x14
  • 54. • Similar • Except res5 (Feature Map size is too Small)
  • 55. • Long-range Multi-hop Communication • Shallow 5-block ResNet50 > Deep baseline ResNet101 • Add NL Blocks > Add Residual Blocks
  • 56. • Add 5 NL Blocks • Spacetime >> Space = Time >> Baseline
  • 57. • NL C2D > I3D (3D ConvNet Baseline) • Smaller Number of FLOPS
  • 58. • Add 5 NL Blocks
  • 59. • 128 Frame (vs 32 Frame)