Deformable Convolutional Networks

Code

• github.com/msracver/Deformable-ConvNets
  OfficialIn papermxnet★ 0
• github.com/open-mmlab/mmdetection
  pytorch★ 32,525
• github.com/pytorch/vision
  pytorch★ 17,584
• github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
  pytorch★ 934
• github.com/esw0116/DynaVSR
  pytorch★ 62
• github.com/qilei123/fpn_crop
  mxnet★ 1
• github.com/zzdxfei/defor_conv_mxnet_code
  mxnet★ 0
• github.com/TangDL/DCN
  tf★ 0
• github.com/chenbys/GuidedOffset
  tf★ 0
• github.com/qilei123/sod_v1_demo
  mxnet★ 0

Abstract

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released.

Tasks

Benchmark Results

Reproductions