1. 什么是ASPP(Atrous Spatial Pyramid Pooling)? ASPP,即空洞空间金字塔池化,是一种用于提取多尺度特征的深度学习技术。它主要用于语义分割等计算机视觉任务中,通过不同膨胀率的空洞卷积来获取不同感受野的特征,进而增强模型的表达能力。 2. ASPP的工作原理 ASPP的工作原理基于空洞卷积(Atrous/Dilated Convolution),...
Atrous Spatial Pyramid Pooling (ASPP) is a semantic segmentation module for resampling a given feature layer at multiple rates prior to convolution. This amounts to probing the original image with multiple filters that have complementary effective fields of view, thus capturing objects as well as ...
针对信号下采样或池化降低分辨率,DeepLab是采用的Atrous(带孔)算法扩展感受野获取更多的上下文信息。 分类器获取以对象中心的决策是需要空间变换的...Convolution在原始模型的顶端增加额外的模块,例如DenseCRF,捕捉像素间长距离信息。SpatialPyramidPooling空间金字塔池化具有不同采样率和多种视野的卷积核,能够以多 ...
在空洞卷积的基础上,提出了空洞空间金字塔池化(atrousSpatial Pyramid Pooling,ASPP)[2],利用不同的空洞率将多个空洞卷积的特征拼接成最终...几个最大值池化层,重新配置网络,使用卷积来重用预先训练好的权值。与添加空洞卷积层来移除池化层不同,更多的空洞卷积层层叠在级联中,进一步增加接受域的大小来覆盖大的对象,...
Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery(高分辨率语义分割) 对Potsdam and Vaihingen 公开数据集进行处理,得到了SOTA的结果,超越DeepLab_v3+,提出的网络结构如下:结合了ASPP和FCN,UNet...
在图像分割领域,图像输入到CNN中,FCN先像传统的CNN那样对图像做卷积再pooling,降低图像尺寸的同时增大感受野,但是由于图像分割预测是pixel-wise的输出,所以要将pooling后较小的图像尺寸upsampling到原始的图像尺寸进行预测(upsampling一般采用deconv反卷积操作),之前的pooling操作使得每个pixel预测都能看到较大感受野信息。因此...
WASP is a novel architecture with Atrous Convolutions that is able to leverage both the larger Field-of-View of the Atrous Spatial Pyramid Pooling configuration and the reduced size of the cascade approach. Figure 3: WASP Module. Examples of the UniPose architecture for Pose Estimation are ...
We propose the “Waterfall Atrous Spatial Pyramid” module, shown in Figure 3. WASP is a novel architecture with Atrous Convolutions that is able to leverage both the larger Field-of-View of the Atrous Spatial Pyramid Pooling configuration and the reduced size of the cascade approach. ...
Deeplab V3+:encoder-decoder with atrous separable convolution for semantic segmentation 语义分割任务对于深度神经网络的使用主要关注于两个模块:空间金字塔池化模块(spatial pyramid pooling module)和编码器解码器结构(encode-decoder structure)。空间金字塔池化模... 查看原文 Deeplab v3+论文笔记 v3 2) Encode-...