Deformable Convolutional Network (2017)

Terry Taewoong Um (terry.t.um@gmail.com)

University of Waterloo

Department of Electrical & Computer Engineering

Terry T. Um

DEFORMABLE

CONVOLUTIONAL NETWORKS

TODAY’S PAPER

ConvolutionRoI pooling

Convolution + learnable offsetRoI pooling + learnable offset

1. INTRODUCTION

- data augmentation

- SIFT (scale invariant feature)

- Label-preserving augmentation?

https://goo.gl/GCf6q8

cs231n, Stanford

https://goo.gl

/fKvx8V

1. INTRODUCTION

There is no reason to use “fixed-size” convolution filters

Introduce learnable offset

Fig.5.

1. INTRODUCTION

Fig.1.

• RoI pooling

https://deepsense.io/region-of-interest-pooling-explained/

1. INTRODUCTION

• Insert simple networks that determine parameters for effective spatial transformations

2. DEFORMABLE CONVNET

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

https://deepsense.io/region-of-interest-pooling-explained/

• Deformable convolution

• Deformable RoI pooling

Any processes that are differentiable can be learned by back propagation

- 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)

3. UNDERSTANDING D-CONVNET

background small obj large obj

Fig.4.

Fig.5.

Table.2.

Fig.6.

3*3 bins deformed

https://github.com/felixlaumon

4. EXPERIMENT

Deformable Convolutional Network (2017)

Data & Analytics

Transcript of Deformable Convolutional Network (2017)

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