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Status: Unchecked ×Keyword: deformable convolution ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Deformable Convolutional Networks

   Dai, Qi, Xiong et al. · IEEE International Conference on Computer Vision (ICCV) · 2017

   The paper introduces deformable convolution and deformable RoI pooling, which learn extra sampling offsets so CNNs can better model geometric transformations, and tests them on detection and segmentation.

   Unchecked2 claims
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   1. UncheckedThe authors report that extensive experiments show their deformable modules work well for object detection and semantic segmentation.“Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation.”
   2. UncheckedThe paper's new deformable modules can replace standard ones in existing CNNs and be trained end-to-end with ordinary back-propagation.“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.”