MobileNetv1是谷歌2017提出的轻量级模型,其基本单元是深度可分离卷积(depthwiseseparableconvolution)。 depthwithconvolution...pointwise卷积。 基本模块: 模型定义如下(其中s2表示步长stride=2下采样,5x即有5倍的,3x3xN dw表示depthwiseseparable深度可分离卷积,其步骤depthwith ...
为了验证这一问题,文章使用Depth-wise卷积替换Swin Transfomer中的所有Local Attention模块,其他结构保持不变(per-LN修改为post-BN),同时为了验证动态DW卷积的效果,文章构建了两种dynamic特性的Depth-wise卷积: (1)D-DW-Conv. 第一种dynamic DW卷积,采用和普通DW卷积相同的权重共享方式,图像空间共享卷积核,通道间独立...
On the Connection between Local Attention and Dynamic Depth-wise Convolution 收录会议: ICLR 2022 Spotlight 论文链接: https://arxiv.org/abs/2106.04263 代码链接: https://github.com/qinzheng93/GeoTransformer 文章更新了在 Large-scale 数据集预训练上 I-D-DW Conv. 的结果,在 ImageNet-22k 预训练,在...
First, we use 3×3 kernels instead of the traditional 5 × 5 kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to...
在Local Attention当中,稀疏连接体现在两个方面:一是Local Attention在图像空间上,每一个output值仅与局部的local window内的input相连接,与ViT的全像素(token)连接不同。二是Local Attention在通道上,每一个output channel仅与一个input chann...
Figure 1: (a) convolution (b) global attention (c) local attention、DW convolution (d) 1x1convolution (e) fully-connected MLP In addition, the paper also presents a relation graph to illustrate the evolution process of certain design principles generated during the model...
一、深度可分离卷积(Depthwise separable convolution) 一些轻量级的网络,如mobilenet中,会有深度可分离卷积depthwise separable convolution,由depthwise(DW)和pointwise(PW)两个部分结合起来,用来提取特征feature m... 一图了解MobileNet的核心:Depthwise separable convolution ...
Xception: Deep Learning with Depthwise Separable Convolutions. arXiv 2017, arXiv:1610.02357. 22. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA...
On the Connection between Local Attention and Dynamic Depth-wise Convolutionarxiv.org/abs/2106.04263 2022.3.31更新: 文章更新了在Large-scale数据集预训练上I-D-DW Conv.的结果,在ImageNet-22k预训练,在ImageNet-1K和ADE20K上finetune的结果表明,DW Conv.的表现在ImageNet1K上略低,而在ADE20K上略高。
在Local Attention当中,稀疏连接体现在两个方面:一是Local Attention在图像空间上,每一个输出值仅与局部的Local Window内的输入相连接,与ViT的全像素(token)连接不同。二是Local Attention在通道上,每一个输出通道仅与一个输入通道连接,没有交叉连接,不同于group convolution与normal convolution。