in Improving Convolutional Networks With Self-Calibrated Convolutions Edit Liu et al. presented self-calibrated convolution as a means to enlarge the receptive field at each spatial location. Self-calibrated convolution is used together with a standard convolution. It first divides the input feature...
First, we introduce self-calibrated convolutions to low-level vision task for the first time to significantly enlarge the receptive field of SR model. Second, Cutblur methods are used to improve the generalization of model. Third, long skip connection was used in model design to improve the ...
CVPR2020 - Improving Convolutional Networks with Self-Calibrated Convolutions(通过自校准卷积改进卷积神经网络) 致力于设计更复杂的体系结构,以增强其表示学习能力。在本文中,我们考虑在不调整模型架构的情况下改进CNN的基本卷积特征转换过程。为此,我们提出了一种新颖的自校准卷积,该卷积通过内部通信显着扩展了每个卷积...
论文题目:Improving Convolutional Networks with Self-Calibrated Convolutions论文地址:mftp.mmcheng.net/Papers 这篇文章设计了一个即插即用的模块来替代传统的卷积层,作者称之为 Self-Calibrated Convolutions(SC)。这个 Self-Calibrated Convolutions主要有两个优点:...
Improving Convolutional Networks with Self-Calibrated Convolutions【阅读笔记】 现在的大部分方法都通过调整网络结构,使网络具有获得更丰富表示特征的能力,如attention、AutoML、NAS。 本文提出的自校准卷积(Self-Calibrated conv),通过增强每一层卷积的能力,提升整个网络的能力。SC conv将原卷积分为多个不同的部分,由于...
The official PyTorch implementation of CVPR 2020 paper "Improving Convolutional Networks with Self-Calibrated Convolutions" - MCG-NKU/SCNet
Before that, to handle large motion across frames, we propose a self-calibrated deformable (SCD) alignment module, in which motion offsets are predicted via self-calibrated convolution that explicitly expand receptive field of each convolutional layer through internal communications in a multi-...
论文链接:Improving Convolutional Networks With Self-Calibrated Convolutions 时间:2020 CVPR2020 作者团队:Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Changhu Wang, Jiashi Feng 分类:计算机视觉--人体关键点检测--2D topdown_heatmap 目录: 1.SCNet背景 ...
总的来说,文中的self-Calibrated Convolutions就是一个多尺度特征提取模块。作者通过特征图下采样来增大CNN的感受野,每个空间位置都可以通过自校准操作融合来自两个不同空间尺度空间的信息。而且,Self-Calibrated Convolutions没有引入额外的可学习参数,但是其计算量还是会增大。
(CNNs), Self-Calibrated convolution is applied to build long-range spatial and inter-channel dependencies around each spatial location that explicitly expand fields-of-view of each convolutional layer through internal communications and hence enriches the output features. By designing the Scale-...