CNN）中引入了学习空间几何形变的能力，得到可变形卷积网络（deformable convolutional networks），...
2. Deformable Convolutional Networks The feature maps and convolution in CNNs are 3D. Both deformable convolution and RoI pooling modules operate on the 2D spatial domain. The operation remains the same across the channel dimension. Without loss of generality, the modules are described in 2D here...
we use the XNOR operation instead of the usual convolutional operation to finish convolution. In de...
[31] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 1 [32] D. Laptev and J. M. Buhmann. Transformation-invariantcon-volutional jungles. In CVPR, 2015. 6 [33] D. Laptev, N. Savinov, J. M. Buhmann,...
This is an official implementation for Deformable Convolutional Networks (Deformable ConvNets) based on MXNet. It is worth noticing that:The original implementation is based on our internal Caffe version on Windows. There are slight differences in the final accuracy and running time due to the ...
This is an official implementation for Deformable Convolutional Networks (Deformable ConvNets) based on MXNet. It is worth noticing that: The original implementation is based on our internal Caffe version on Windows. There are slight differences in the final accuracy and running time due to the ple...
{Deformable Convolutional Networks}, Journal = {arXiv preprint arXiv:1703.06211}, Year = {2017} } @inproceedings{dai16rfcn, Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun}, Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks}, Conference = {NIPS}, Year...
Nevertheless, the spatial support of these networks may be inexact because the offsets are learned implicitly via extra convolutional layer. In this work, we present curvature-driven deformable convolutional networks (C-DCNets) that adopt explicit geometric property of the preceding feature maps to ...
By contrast, deep learning approaches [10, 19, 14], based on Convolutional Networks [11], extract strong image features, but do not explicitly model object composition. Instead, they rely on pooling and large fully connected layers to combine information from spatially dis- parate regions; these...
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. CoRR abs/1703.06211 (2017) Google Scholar Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of 2005 IEEE Computer Society Conference on...