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...
1 DenseNet：比ResNet更优的CNN模型 2 Review: DenseNet — Dense Convolutional Network (Image Cl...
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 ...
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 ...
Therefore, it uses a 1 × 1 convolutional layer to reduce the channel dimension. Then, it is followed by two parallel branches with two 3 × 3 convolutional layers for class prediction and regression prediction, respectively. The regression branch is composed of two paratactic branches ...
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...
The resulting CNNs are called deformable convolutional networks, or deformable ConvNets.两个模块都轻量的。它们为偏移学习增加了少量的参数和计算。他们可以很容易地取代深层CNN中简单的对应部分,并且可以很容易地通过标准的反向传播进行端对端的训练。所得到的CNN被称为可变形卷积网络,或可变形ConvNets。
Deformable Convolutional Networks笔记 Deformable convolution network由两个模块构成,一个是deformable convolution,一个是 deformable RoI pooling。 CNN中在特征图上的卷积操作是三维的,即平面加通道。而deformable convolution和deformable RoI pooling则是二维空间的的,他们改变卷积在平面上的采样位置,即感受野的位置,而...
Deformable Convolutional Networks 摘要 卷积神经网络(CNN)由于其构建模块固定的几何结构天然地局限于建模几何变换。在这项工作中,我们引入了两个新的模块来提高CNN的转换建模能力,即可变形卷积和可变形RoI池化。两者都基于这样的想法:增加模块中的空间采样位置以及额外的偏移量,并且从目标任务中学习偏移量,而不需要额外...
{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...