更新していく予定です。
セグメンテーションのCNNを使った手法をメモしていきます。
SegmentationとCNNのメモ
FCN(Fully Convolution Network)
論文

Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies offeatures. We show that convolutional networks by themselves, trainedend-to-end, pixel...
SegNet
論文

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We present a novel and practical deep fully convolutional neural networkarchitecture for semantic pixel-wise segmentation termed SegNet. This coretrainable segm...
U-Net
論文

U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requiresmany thousand annotated training samples. In this paper, we present a networkand traini...
モデル
U-Net: Convolutional Networks for Biomedical Image Segmentation
UNet++
論文

UNet++: A Nested U-Net Architecture for Medical Image Segmentation
In this paper, we present UNet++, a new, more powerful architecture formedical image segmentation. Our architecture is essentially a deeply-supervisedencoder-de...
モデル
PSPNet
論文

Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diversescenes. In this paper, we exploit the capability of global context informationby differ...
モデル
RefineNet
論文

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Recently, very deep convolutional neural networks (CNNs) have shownoutstanding performance in object recognition and have also been the firstchoice for dense cl...
Highly Fused Convolutional Network
論文

Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Semantic image segmentation is one of the most challenged tasks in computervision. In this paper, we propose a highly fused convolutional network, whichconsists...