Over the past decade, CNNs have achieved remarkable success in computer vision, largely due to their ability to autonomously learn both local and global features through convolution operations. When processing
1)设计了Omni-Scale Residual Block,本质就是加权版多尺度特征融合,Inception升级版。 2)使用了Depthwise Separable Convolutions,成了lightweight network。网络架构:实验:感觉实验结果能成为SOTA主要是因为CNN backbone不同,说明了多尺度信息的重要性,远古的带Inception的GoogleNet确实是很强大的,这篇论文的核心就是加权...
To efficiently balance model complexity and performance, we propose a multi-scale attention network (MSAN) by cascading multiple multi-scale attention blocks (MSAB), each of which integrates a multi-scale cross block (MSCB) and a multi-path wide-activated attention block (MWAB). Specifically, ...
1.研究背景与意义 研究背景与意义:金属工件是现代工业生产中不可或缺的重要组成部分。金属工件的质量和性能直接影响到产品的品质和效率,因此对金属工件的研究和改进具有重要的意义。随着科技的不断进步和工业的发展,对金属工件的要求也越来越高,传统的金属加工方法已经无法满足现代工业的需求。因此,寻找原创创新点来改进...
Progressive Multi-Scale Convolutional Block (PMCB)The core of CLPSTNet is the Progressive Multi-Scale Convolutional Block (PMCB), which is implemented in the clpstnet.py file.Encoder NetworkThe encoder network ( Encoder_Network in clpstnet.py ) is responsible for hiding secret information within...
In Fig. 2b, MS-DAM concatenates the feature maps of multi-scale DCNs with three kernel sizes and performs a residual connection using a 1 × 1 convolution layer to prevent gradient-vanishing and preserve hierarchical information. When the kernel size is small, low-frequency information, suc...
What are the advantages of using Convolution Block Attention Module (CBAM) in Local Climate Zone classification? What were the classification results for Local Climate Zones in different cities? Why is there a need for modifications in ResNets for Local Climate Zone classification? What are the ben...
On the basis of residual learning, a multi-scale fractal residual block (MSFRB) is designed. This block uses convolution kernels of different sizes to extract image multi-scale features and uses multiple paths to extract and fuse image features of different depths. Then, the shallow features ...
网络最前面是分辨率最低的子网络(coarest level network),在这个子网络最后,是“upconvolution layer”,将重建的低分辨率图像放大为高分辨率图像,然后和高一层的子网络的输入连接在一起,作为上层网络的输入。 再看单个 CNN 的结构:在第一层卷积层后,叠加了19个 ResBlock,最后一个卷积层将feature map 转化为输出...
Seasonal Prediction Block Multi-scale Isometric Convolution(MIC) Layer 实验结果 Comments 论文链接: MICN: Multi-scale Local and Global Context Modeling for Long-term...openreview.net/forum?id=zt53IDUR1U 本文中了2023 ICLR的oral。又是一篇长时间序列预测的文章,但是它是一个基于时域卷积模块的模型,...