Deformable Convolutional Network (2017)
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Transcript of Deformable Convolutional Network (2017)
![Page 1: Deformable Convolutional Network (2017)](https://reader031.fdocuments.net/reader031/viewer/2022020213/58f9a8df760da3da068b68a8/html5/thumbnails/1.jpg)
Terry Taewoong Um ([email protected])
University of Waterloo
Department of Electrical & Computer Engineering
Terry T. Um
DEFORMABLE
CONVOLUTIONAL NETWORKS
1
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TODAY’S PAPER
Terry Taewoong Um ([email protected])
ConvolutionRoI pooling
Convolution + learnable offsetRoI pooling + learnable offset
![Page 3: Deformable Convolutional Network (2017)](https://reader031.fdocuments.net/reader031/viewer/2022020213/58f9a8df760da3da068b68a8/html5/thumbnails/3.jpg)
1. INTRODUCTION
Terry Taewoong Um ([email protected])
- data augmentation
- SIFT (scale invariant feature)
- Label-preserving augmentation?
https://goo.gl/GCf6q8
cs231n, Stanford
https://goo.gl
/fKvx8V
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1. INTRODUCTION
Terry Taewoong Um ([email protected])
There is no reason to use “fixed-size” convolution filters
Introduce learnable offset
Fig.5.
![Page 5: Deformable Convolutional Network (2017)](https://reader031.fdocuments.net/reader031/viewer/2022020213/58f9a8df760da3da068b68a8/html5/thumbnails/5.jpg)
1. INTRODUCTION
Terry Taewoong Um ([email protected])
Fig.1.
• RoI pooling
https://deepsense.io/region-of-interest-pooling-explained/
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1. INTRODUCTION
Terry Taewoong Um ([email protected])
?
• Insert simple networks that determine parameters for effective spatial transformations
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2. DEFORMABLE CONVNET
Terry Taewoong Um ([email protected])
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
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2. DEFORMABLE CONVNET
Terry Taewoong Um ([email protected])
https://deepsense.io/region-of-interest-pooling-explained/
![Page 9: Deformable Convolutional Network (2017)](https://reader031.fdocuments.net/reader031/viewer/2022020213/58f9a8df760da3da068b68a8/html5/thumbnails/9.jpg)
2. DEFORMABLE CONVNET
Terry Taewoong Um ([email protected])
• Deformable convolution
• Deformable RoI pooling
Any processes that are differentiable can be learned by back propagation
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2. DEFORMABLE CONVNET
Terry Taewoong Um ([email protected])
- 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)
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3. UNDERSTANDING D-CONVNET
Terry Taewoong Um ([email protected])
background small obj large obj
Fig.4.
Fig.5.
Table.2.
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Terry Taewoong Um ([email protected])