FCN(Fully Convolution Network)
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pix...
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable se...
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and trai...
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-...
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by diff...
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense ...
Highly Fused Convolutional Network
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Semantic image segmentation is one of the most challenged tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consis...