《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》 期刊:TPAMI 核心思想:存储编码器最大池化层中最大值的索引,上采样时,将特征图根据存储的索引对其恢复,再对其卷积。…
【论文阅读】SegNet A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,程序员大本营,技术文章内容聚合第一站。
decode网络中的decoder 利用对应encoder feature map中保存的max-index对输入的feature map进行上采样,产生的稀疏feature maps后接一系列可训练的卷积核,输出密集的feature maps,后接BN用于规范化处理正则化减弱过拟合,与输入对应的decoder产生多通道feature map,虽然输入只有(RGB)三通道。其他的encoder,decoder的通道数,...
SegNet的encoder network为vgg16(移除了全连接层,既提高了encoder network最终输出的分辨率,又大大减少了参数,从134M减少到14.7M),在decoder network里依靠encoder network的max pooling层所产生的最大值位置索引(the locations of the maximum feature value in each pooling window )对输入进行upsample(无需学习),将...
在另一个极端,我们可以在每一层添加64个编码器特征映射到SegNet解码器中的相应输出特征映射,以创建一个更内存密集型的SegNet变体(SegNet-basic encoder addition)。这里使用了两个用于上采样的池化索引,然后是一个卷积步骤来强化其稀疏输入。然后将其按元素顺序添加到相应的编码器特征映射中,以产生解码器输出。
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation 2015CVPR Encoder-Decoder Architecture 编码器:通过输入的图像,得到该图像的特征图谱。 解码器:根据提供的特征图谱,实现每个像素的类别预测。 (分割任务中通常使用分类任务中已经训练好的网络,编码器部分大多使用迁移学习,解码器很大程度上...
摘要原文 We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The archi...
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation---2016 SegNet论文解读,程序员大本营,技术文章内容聚合第一站。
Deep Convolutional Encoder–Decoder Models for Road Extraction from Aerial Imagerydoi:10.1007/978-981-99-9486-1_1Road extraction from aerial imagery is not a trivial task. It plays a pivotal role in urban planning, navigation, disaster assessment and various other fields. It poses challenges due ...
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forwa... V Badrinarayanan,A Handa,R Cipolla - 《...