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...
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 ...
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。 Experiment 1.比较ImageNet-1k数据集上使用...
这个文章是Improving Convolutional Networks with Self-Calibrated Convolutions。这是一个即插即用的一个模块,挺好的。这个模块主要是用来增大网络的感受野的。另外这个模块也可以不需要增加太多的参数就可以获得这个效果。整体还是比较有效的。 整篇 论文 的核心大概就是这幅图了。......
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 ...
《Improving Convolutional Networks with Self-calibrated Convolutions》是2020年CVPR的论文,作者来自于南开大学程明明团队。最近各种卷积注意力组合的模块工作层出不穷,性能涨点明显,包括之前的Res2Net、李沐团队的ResNeSt,应该是近期的热点方向。 论文地址:http://mftp.mmcheng.net/Papers/2... ...
The official PyTorch implementation of CVPR 2020 paper "Improving Convolutional Networks with Self-Calibrated Convolutions" - Castile/SCNet
Radiologists use the standardized Lung-RADS clinical scoring criteria to assess and report spiculations/lobulations and sharp/curved spikes on the surface of lung nodules, because they are good predictors of lung cancer. Manual spiculation/lobulation annotation and classification is a tedious task fo...
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-...