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Deformable Convolutional Network (2017)

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"Deformable Convolutional Networks",  J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, 2017.
[Link] https://arxiv.org/abs/1703.06211

Published in: Data & Analytics
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Deformable Convolutional Network (2017)

  1. 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry T. Um DEFORMABLE CONVOLUTIONAL NETWORKS 1
  2. 2. TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) Convolution RoI pooling Convolution + learnable offset RoI pooling + learnable offset
  3. 3. 1. INTRODUCTION Terry Taewoong Um (terry.t.um@gmail.com) - data augmentation - SIFT (scale invariant feature) - Label-preserving augmentation? https://goo.gl/GCf6q8 cs231n, Stanford https://goo.gl /fKvx8V
  4. 4. 1. INTRODUCTION Terry Taewoong Um (terry.t.um@gmail.com) There is no reason to use “fixed-size” convolution filters  Introduce learnable offset Fig.5.
  5. 5. 1. INTRODUCTION Terry Taewoong Um (terry.t.um@gmail.com) Fig.1. • RoI pooling https://deepsense.io/region-of- interest-pooling-explained/
  6. 6. 1. INTRODUCTION Terry Taewoong Um (terry.t.um@gmail.com) ? • Insert simple networks that determine parameters for effective spatial transformations
  7. 7. 2. DEFORMABLE CONVNET Terry Taewoong Um (terry.t.um@gmail.com) x(3.7,2.3) = 0.7*0.3*x(4.0,3.0) + 0.7*0.7*x(4.0,2.0) + … • Bilinear interpolation
  8. 8. 2. DEFORMABLE CONVNET Terry Taewoong Um (terry.t.um@gmail.com) https://deepsense.io/region-of- interest-pooling-explained/
  9. 9. 2. DEFORMABLE CONVNET Terry Taewoong Um (terry.t.um@gmail.com) • Deformable convolution • Deformable RoI pooling  Any processes that are differentiable can be learned by back propagation
  10. 10. 2. DEFORMABLE CONVNET Terry Taewoong Um (terry.t.um@gmail.com) - Deep Lab : SOTA semantic segmentator - Category-aware RPN : a simplified SSD - Faster R-CNN : SOTA object detector - R-FCN : SOTA object detector (per-RoI computation cost ) (I hope other members will have a chance to present on these SOTA methods in the near future)
  11. 11. 3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com) background small obj large obj Fig.4. Fig.5. Table.2.
  12. 12. 3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com) Fig.6. 3*3 bins  deformed
  13. 13. 3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com) https://github.com/felixlaumon
  14. 14. 3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com)
  15. 15. 3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com)
  16. 16. 4. EXPERIMENT Terry Taewoong Um (terry.t.um@gmail.com)
  17. 17. Terry Taewoong Um (terry.t.um@gmail.com)

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