在FCN網絡在2104年提出后，越來越多的關于圖像分割的深度學習網絡被提出，相比傳統(tǒng)方法，這些網絡效果更好，運算速度更快，已經能成熟的運用在自然圖像上。語義分割顯然已經是計算機視覺領域的一個熱門研究領域，也是通往實現完全場景理解的道路之一，被廣泛應用于無人駕駛、人機交互、醫(yī)療圖像、計算攝影、圖像搜索引擎、增強現實等應用領域。語義分割是像素級分類問題，將同一類物體像素點歸為一類，如圖所示。 左：輸入圖像，右：輸出分割圖像 ? ? ? ? ? 存在的挑戰(zhàn)：1.池化或者卷積步長造成的特征圖分辨率減小；2.圖像中存在不同尺度的目標；3.錯誤匹配關系；4.類別混淆；5.類別不明顯。 ? ? ? ? ? 方法：1.dilated convolution；2.圖像金字塔；3.編碼解碼結構；4.級聯(lián)結構；5.空間金字塔池化。 1.數據集 2D數據集 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數據集
1.15?NYUDv2 1.16?SUN3D 1.17?SUNRGBD 1.18?RGB-D Object Dataset 3D數據集 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.圖像標注工具 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

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