Multi-scale context aggregation by dilated convolutions——通过膨胀卷积进行多尺度上下文信息的聚合 我读完这篇论文感觉可以概括的分为:提出了 膨胀卷积膨胀卷积 、运用膨胀卷积进行了多尺度预测、设置了一个Front-end(然后将其和multi-scale部分相结合) Abstract The idea of Dilated Convolution is come from the ...
注意这里 dilated convolution 的参数数量是相同的,都是 3*3=9 Multi-scale context aggregation: The basic context module has 7 layers that apply 3×3 convolutions with different dilation factors. The dilations are 1, 1, 2, 4, 8, 16, and 1。 这里主要通过不同的 different dilation factors 得...
最近在读‘MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS’这篇论文,里面提到了DILATED CONVOLUTIONS,即空洞卷积,下面我就来介绍一下它的基本原理。 Firstly,我们应该知道普通卷积的基本操作过程,如下图所示: 上图是具体的计算过程,卷积核为单个,我们接下来看普通卷积感受野的理解: 上图中输...猜...
We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy....
重温Dilated Convolution膨胀卷积,对论文《MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS》中采用Dilation后的感受野计算示意图产生了迷惑,于是自己重新画图琢磨了一番。 可以看到作者的感受野计算是递进式的,即F1在F0的基础上经3x3,dilation=1卷积得到,即F2在F1的基础上经3x3,dilation=...AI...
论文阅读笔记二十一:MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS(ICRL2016) 论文源址:https://arxiv.org/abs/1511.07122 tensorflow Github:https://github.com/ndrplz/dilation-tensorflow 摘要 该文提出了空洞卷积模型,在不降低分辨率的基础上聚合图像中不同尺寸的上下文信息,同时,空洞卷积扩大感受野的...
MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS这篇论文是ICLR 2016会议文章,这里简短记录下论文的主要内容。时间精力有限,只是粗读了下论文的网络结构,难免有纰漏。 论文应该借鉴了Deeplab提出的带dilation(hole)的卷积层,特别是针对语义分割这样子的dense prediction,采用了带dilation的卷积层,能够融合多尺度的...
实验结果显示，膨胀卷积在测试集上比FCN-8s和DeepLabv1有约5%的性能提升。进一步的消融研究显示，Dilated Convolution无论基本还是大型版本，都能有效改进结果，并与后处理步骤兼容。然而，遮挡物体等情况下，分割效果可能受到影响。论文还探讨了在CamVid、KITTI和Cityscapes等数据集上的应用。
State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work,
例如,CATNet(Contextual Aggregation Network)是一种用于遥感图像实例分割的框架,它利用三个轻量级的即插即用模块(密集特征金字塔网络、空间上下文金字塔和层次兴趣区域提取器)来聚合多尺度上下文。 以下是CATNet中密集特征金字塔网络(DenseFPN)的一个简化代码示例,用于展示如何实现特征融合: python import torch import ...