CNN for Semantic Segmentation（语义分割，论文，代码，数据集，标注工具，blog）
在FCN网络在2104年提出后，越来越多的关于图像分割的深度学习网络被提出，相比传统方法，这些网络效果更好，运算速度更快，已经能成熟的运用在自然图像上。语义分割显然已经是计算机视觉领域的一个热门研究领域，也是通往实现完全场景理解的道路之一，被广泛应用于无人驾驶、人机交互、医疗图像、计算摄影、图像搜索引擎、增强现实等应用领域。语义分割是像素级分类问题，将同一类物体像素点归为一类，如图所示。
左：输入图像，右：输出分割图像
存在的挑战：1.池化或者卷积步长造成的特征图分辨率减小；2.图像中存在不同尺度的目标；3.错误匹配关系；4.类别混淆；5.类别不明显
。
方法：1.dilated convolution；2.图像金字塔；3.编码解码结构；4.级联结构；5.空间金字塔池化。
1.数据集
2D数据集
1.1 PASCAL Visual Object Classes (VOC)
1.2 PASCAL Context
1.3 PASCAL Part
1.4 Semantic Boundaries Dataset (SBD)
1.5 Microsoft Common Objects in Context (COCO)
1.6 SYNTHetic Collection of Imagery and Annotations (SYNTHIA)
1.7 Cityscapes
1.8 CamVid
1.9 Youtube-Objects
1.10 Adobe’s Portrait Segmentation Adobe’s Portrait Segmentation
1.11 Materials in Context (MINC)
1.12 Densely-Annotated VIdeo Segmentation (DAVIS)
1.13 Stanford background
1.14 SiftFlow
2.5D数据集
1.15 NYUDv2
1.16 SUN3D
1.17 SUNRGBD
1.18 RGB-D Object Dataset
3D数据集
1.19 ShapeNet Part
1.20 Stanford 2D-3D-S
1.21 A Benchmark for 3D Mesh Segmentation
1.22 Sydney Urban Objects Dataset
1.23 Large-Scale Point Cloud Classification Benchmark
2.图像标注工具
2.1 labelme: Image Annotation Tool with Python
2.2 labelImgPlus
2.3 PS
2.4 OpenSurfaces Segmentation UI
2.5 ImageSegmentation
2.6 JS Segment Annotator
3. Papers
2017
LinkNet
https://arxiv.org/pdf/1707.03718.pdf
https://github.com/e-lab/LinkNet
ICNet
https://arxiv.org/pdf/1704.08545.pdf
https://github.com/hszhao/ICNet
DeepLabv3
https://arxiv.org/pdf/1706.05587v3.pdf
Mask-RCNN
https://arxiv.org/pdf/1703.06870.pdf
https://github.com/jasjeetIM/Mask-RCNN
ERFNet
http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf
https://github.com/Eromera/erfnet
Large Kernel Matters
https://arxiv.org/pdf/1703.02719
2016
Fully-Convolutional Network (FCN)
https://arxiv.org/pdf/1605.06211.pdf
https://github.com/shelhamer/fcn.berkeleyvision.org
DeepLab
https://arxiv.org/pdf/1606.00915.pdf
https://bitbucket.org/deeplab/deeplab-public/
ENet
https://arxiv.org/pdf/1606.02147.pdf
https://github.com/TimoSaemann/ENet
PixelNet
https://arxiv.org/pdf/1609.06694.pdf
https://github.com/aayushbansal/PixelNet
RefineNet
https://arxiv.org/pdf/1611.06612.pdf
https://github.com/guosheng/refinenet
PSPNet
https://arxiv.org/pdf/1612.01105.pdf
https://github.com/hszhao/PSPNet
FCIS
https://arxiv.org/pdf/1611.07709.pdf
https://github.com/msracver/FCIS
MultiNet
https://arxiv.org/pdf/1612.07695.pdf
https://github.com/MarvinTeichmann/MultiNet
2015
U-Net
https://arxiv.org/pdf/1505.04597.pdf
https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
SegNet
https://arxiv.org/pdf/1511.00561.pdf
https://github.com/alexgkendall/caffe-segnet
DilatedNet
https://arxiv.org/pdf/1511.07122.pdf
https://github.com/nicolov/segmentation_keras
DeepMask
https://arxiv.org/pdf/1506.06204.pdf
https://github.com/facebookresearch/deepmask
CRFasRNN
http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf
https://github.com/torrvision/crfasrnn
Dilated convolution
https://arxiv.org/pdf/1511.07122.pdf
https://github.com/fyu/dilation
DeconvNet
https://arxiv.org/pdf/1505.04366.pdf
https://github.com/HyeonwooNoh/DeconvNet
MNC
https://arxiv.org/pdf/1512.04412.pdf
https://github.com/daijifeng001/MNC
Zoomout Semantic Segmentation
https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf
https://bitbucket.org/m_mostajabi/zoom-out-release
4.Blog
A 2017 Guide to Semantic Segmentation with Deep Learning
Semantic Segmentation using Fully Convolutional Networks over the years