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Deformable Convolutional Network (2017)

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Terry Taewoong Um
Terry Taewoong Um

Terry Taewoong Um proposes deformable convolutional networks. The document discusses introducing learnable offsets to convolutional filters and region of interest pooling layers to allow the networks to spatially transform based on the input data. This helps the networks better adapt to objects of different scales and aspect ratios. Experimental results show deformable convnets achieve state-of-the-art performance on semantic segmentation, object detection and other tasks. Code is available online for others to experiment with these techniques.

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Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry T. Um DEFORMABLE CONVOLUTIONAL NETWORKS 1
TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) Convolution RoI pooling Convolution + learnable offset RoI pooling + learnable offset
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
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.
1. INTRODUCTION Terry Taewoong Um (terry.t.um@gmail.com) Fig.1. • RoI pooling https://deepsense.io/region-of- interest-pooling-explained/
1. INTRODUCTION Terry Taewoong Um (terry.t.um@gmail.com) ? • Insert simple networks that determine parameters for effective spatial transformations
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
2. DEFORMABLE CONVNET Terry Taewoong Um (terry.t.um@gmail.com) https://deepsense.io/region-of- interest-pooling-explained/
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
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)
3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com) background small obj large obj Fig.4. Fig.5. Table.2.
3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com) Fig.6. 3*3 bins  deformed
3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com) https://github.com/felixlaumon
3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com)
3. UNDERSTANDING D-CONVNET Terry Taewoong Um (terry.t.um@gmail.com)
4. EXPERIMENT Terry Taewoong Um (terry.t.um@gmail.com)
Terry Taewoong Um (terry.t.um@gmail.com)

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