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。 (2)权重共享——意...
(2) We propose a local perception feed-forward network to substitute for the feed-forward network layer in each encoder block, which employs the depth-wise convolution to enhance the correlation of neighbour information in the spatial dimension. Our modifications contribute to analyzing poses that ...
二是 Local Attention 在通道上,每一个 output channel 仅与一个 input channel 连接,没有交叉连接,不同于 group convolution 与 normal convolution。 2)权重共享:意味着有一些连接的权重是相同且共享的,它降低了模型的参数量,同时不需要增加训练数据即可增强模型。在模型中,一个权重被共享使用,可以被认为针对该权...
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...
adopts the same weight sharing method as ordinary depth-wise convolution: spatial space shared convolution kernel and independent convolution kernel between channels. It also uses Global Average Pooling to process input features and then dynamically predicts dynamic convol...
the absolute value layer, data augmentation and the domain knowledge. However, some designing of the network structure were not extensively studied so far, such as different convolutions (inception, xception, etc.) and variety ways of pooling(spatial pyramid pooling, etc.). In this paper, we ...
(1)针对石化加热炉低氧燃烧控制问题,本文首先提出一种基于注意力机制的深度可分离卷积 LSTM(Attention with Depth-wise separable convolution LSTM,ADSC-LSTM)网络,... 郭其亮 - 西安理工大学 被引量: 0发表: 2023年 Towards Surgical Tools Detection and Operative Skill Assessment Based on Deep Learning We pr...
Transformer的文章近两年来可谓是井喷式爆发,大量工作来设计各种任务上的transformer模型,然而,attention作为transformer的核心模块,真的比卷积强吗?这篇文章为你带来了local attention和dynamic depth-wise convolution的新视角,其实设计好的卷积结构,并不比transformer差!
今天的这篇文章或许能带给你新视角,微软亚洲研究院的研究员们从Local Attention和Dynamic Depth-wise Convolution的视角出发发现,设计好的卷积结构并不比Transformer差!相关论文“On the Connection between Local Attention and Dynamic Depth-wise Convolution”已被ICLR 2022收录。