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 ...
We propose a multi-scale aggregation model framework to deal with volume-varied lesions. Firstly, we present a specifically-designed network for small lesion segmentation called Stack-Net, in which multiple convolutional layers are 'one-by-one' connected, aiming to preserve rich local spatial ...
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...
对于移除的池化层后接的卷积层的dilation factor扩大2倍。因此,最后一层的卷积层的的dilated factor扩大为4。通过空洞卷积,可以利用原始分类网络的参数初始化,同时产生更高分辨率的输出。该模型,在Pascal VOC2012数据集上进行训练,基于SGD优化方法,mini-batch 大小为14,学习率为1e-3,动量大小为0.9,迭代60000次。 实...
提出了Omni-Scale Network (OSNet),可以实现omni-scale feature learning;设计了一个residual block,包括多个卷积特征的分支,每一个分支可以检测到某一种尺度的特征;设计了一个aggregation gate用于多种尺度特征的融合,本质是加权融合with input-dependent channel-wise weights; 为了可以学习spatial-channel correlations并...
论文中的多尺度信息聚合模块基于膨胀卷积构建，初始尝试使用标准或随机初始化的卷积核效果不佳，作者采用身份内核，确保信息逐层传递。对于基本和大型上下文模块，初始化规则根据输入和输出通道数进行调整，确保信息的有效融合。Front-end部分，作者选择了VGG-16作为基础模型，通过去除最后的池化层和striding，...
Multi-scale Patch Aggregation (MPA)for Simultaneous Detection and Segmentation ∗Shu Liu † Xiaojuan Qi † Jianping Shi ♭ Hong Zhang † Jiaya Jia ††The Chinese University of Hong Kong♭SenseTime Group Limited{sliu, xjqi, hzhang, leojia}@cse.cuhk.edu.hk shijianping@sensetime....
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 得到 multi-scale context。
We deliver a multiscale aggregation (MA) to adaptively fuse features in different hierarchies to benefit semantic information under diverse receptive filed. The SIM and MA are meant to be complementary modules to guide the model in learning an accurate density map. Extensive experiments on benchmark...
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution(阅读文献)10.12 基于变压器的磁共振多对比度超分辨率多尺度背景匹配与聚合 摘要:MRI可以显示相同解剖结构的多对比图像,使多对比超分辨率(SR)技术成为可能。和使用单一对比的SR重建相比,多对比SR重建通过利用嵌...