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【专知荟萃20】图像分割Image Segmentation知识资料全集（入门/进阶/论文/综述/视频/专家，附查看）

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  • 图像分割 （Image Segmentation) 专知荟萃

    • 入门学习

    • 进阶论文

    • 综述

    • Tutorial

    • 视频教程

    • 代码

    • Semantic segmentation

    • Instance aware segmentation

    • Satellite images segmentation

    • Video segmentation

    • Autonomous driving

    • Annotation Tools:

    • Datasets

    • 比赛

    • 领域专家


入门学习

  1. A 2017 Guide to Semantic Segmentation with Deep Learning 概述——用深度学习做语义分割

    • [http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review]

    • 中文翻译：[http://simonduan.site/2017/07/23/notes-semantic-segmentation-deep-learning-review/]

  2. 从全卷积网络到大型卷积核：深度学习的语义分割全指南

    • [https://www.jiqizhixin.com/articles/2017-07-14-10]

  3. Fully Convolutional Networks

    • [http://simtalk.cn/2016/11/01/Fully-Convolutional-Networks/]

  4. 语义分割中的深度学习方法全解：从FCN、SegNet到各代DeepLab

    • [https://zhuanlan.zhihu.com/p/27794982]

  5. 图像语义分割之FCN和CRF

    • [https://zhuanlan.zhihu.com/p/22308032]

  6. 从特斯拉到计算机视觉之「图像语义分割」

    • [http://www.52cs.org/?p=1089]

  7. 计算机视觉之语义分割

    • [http://blog.geohey.com/ji-suan-ji-shi-jue-zhi-yu-yi-fen-ge/]

  8. Segmentation Results: VOC2012 PASCAL语义分割比赛排名

    • [http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6]


进阶论文

  1. U-Net [https://arxiv.org/pdf/1505.04597.pdf]

  2. SegNet [https://arxiv.org/pdf/1511.00561.pdf]

  3. DeepLab [https://arxiv.org/pdf/1606.00915.pdf]

  4. FCN [https://arxiv.org/pdf/1605.06211.pdf]

  5. ENet [https://arxiv.org/pdf/1606.02147.pdf]

  6. LinkNet [https://arxiv.org/pdf/1707.03718.pdf]

  7. DenseNet [https://arxiv.org/pdf/1608.06993.pdf]

  8. Tiramisu [https://arxiv.org/pdf/1611.09326.pdf]

  9. DilatedNet [https://arxiv.org/pdf/1511.07122.pdf]

  10. PixelNet [https://arxiv.org/pdf/1609.06694.pdf]

  11. ICNet [https://arxiv.org/pdf/1704.08545.pdf]

  12. ERFNet [http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf]

  13. RefineNet [https://arxiv.org/pdf/1611.06612.pdf]

  14. PSPNet [https://arxiv.org/pdf/1612.01105.pdf]

  15. CRFasRNN [http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf]

  16. Dilated convolution [https://arxiv.org/pdf/1511.07122.pdf]

  17. DeconvNet [https://arxiv.org/pdf/1505.04366.pdf]

  18. FRRN [https://arxiv.org/pdf/1611.08323.pdf]

  19. GCN [https://arxiv.org/pdf/1703.02719.pdf]

  20. DUC, HDC [https://arxiv.org/pdf/1702.08502.pdf]

  21. Segaware [https://arxiv.org/pdf/1708.04607.pdf]

  22. Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf]



综述

  1. A Review on Deep Learning Techniques Applied to Semantic Segmentation Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Jose Garcia-Rodriguez 2017

    • [https://arxiv.org/abs/1704.06857]

  2. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

    • [https://arxiv.org/abs/1704.05519]

  3. 基于内容的图像分割方法综述 姜 枫 顾 庆 郝慧珍 李 娜 郭延文 陈道蓄 2017

    • [http://www.jos.org.cn/ch/reader/create_pdf.aspx?file_no=5136&journal_id=jos\]


Tutorial

  1. Semantic Image Segmentation with Deep Learning

    • [http://www.robots.ox.ac.uk/~sadeep/files/crfasrnn_presentation.pdf\]

  2. A 2017 Guide to Semantic Segmentation with Deep Learning

    • [http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review]

  3. Image Segmentation with Tensorflow using CNNs and Conditional Random Fields

    • [http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/18/image-segmentation-with-tensorflow-using-cnns-and-conditional-random-fields/]


视频教程

  1. CS231n: Convolutional Neural Networks for Visual Recognition Lecture 11 Detection and Segmentation 

    • [http://cs231n.stanford.edu/syllabus.html]

  2. Machine Learning for Semantic Segmentation - Basics of Modern Image Analysis

    • [https://www.youtube.com/watch?v=psLChcm8aiU]


代码

Semantic segmentation

  1. U-Net (https://arxiv.org/pdf/1505.04597.pdf)

    • https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ (Caffe - Matlab)

    • https://github.com/jocicmarko/ultrasound-nerve-segmentation (Keras)

    • https://github.com/EdwardTyantov/ultrasound-nerve-segmentation (Keras)

    • https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model (Keras)

    • https://github.com/yihui-he/u-net (Keras)

    • https://github.com/jakeret/tf_unet (Tensorflow)

    • https://github.com/DLTK/DLTK/blob/master/examples/Toy_segmentation/simple_dltk_unet.ipynb (Tensorflow)

    • https://github.com/divamgupta/image-segmentation-keras (Keras)

    • https://github.com/ZijunDeng/pytorch-semantic-segmentation (PyTorch)

    • https://github.com/akirasosa/mobile-semantic-segmentation (Keras)

    • https://github.com/orobix/retina-unet (Keras)

  2. SegNet (https://arxiv.org/pdf/1511.00561.pdf)

    • https://github.com/alexgkendall/caffe-segnet (Caffe)

    • https://github.com/developmentseed/caffe/tree/segnet-multi-gpu (Caffe)

    • https://github.com/preddy5/segnet (Keras)

    • https://github.com/imlab-uiip/keras-segnet (Keras)

    • https://github.com/andreaazzini/segnet (Tensorflow)

    • https://github.com/fedor-chervinskii/segnet-torch (Torch)

    • https://github.com/0bserver07/Keras-SegNet-Basic (Keras)

    • https://github.com/tkuanlun350/Tensorflow-SegNet (Tensorflow)

    • https://github.com/divamgupta/image-segmentation-keras (Keras)

    • https://github.com/ZijunDeng/pytorch-semantic-segmentation (PyTorch)

    • https://github.com/chainer/chainercv/tree/master/examples/segnet (Chainer)

    • https://github.com/ykamikawa/keras-SegNet (Keras)

  3. DeepLab (https://arxiv.org/pdf/1606.00915.pdf)

    • https://bitbucket.org/deeplab/deeplab-public/ (Caffe)

    • https://github.com/cdmh/deeplab-public (Caffe)

    • https://bitbucket.org/aquariusjay/deeplab-public-ver2 (Caffe)

    • https://github.com/TheLegendAli/DeepLab-Context (Caffe)

    • https://github.com/msracver/Deformable-ConvNets/tree/master/deeplab (MXNet)

    • https://github.com/DrSleep/tensorflow-deeplab-resnet (Tensorflow)

    • https://github.com/muyang0320/tensorflow-deeplab-resnet-crf (TensorFlow)

    • https://github.com/isht7/pytorch-deeplab-resnet (PyTorch)

    • https://github.com/bermanmaxim/jaccardSegment (PyTorch)

    • https://github.com/martinkersner/train-DeepLab (Caffe)

    • https://github.com/chenxi116/TF-deeplab (Tensorflow)

  4. FCN (https://arxiv.org/pdf/1605.06211.pdf)

    • https://github.com/vlfeat/matconvnet-fcn (MatConvNet)

    • https://github.com/shelhamer/fcn.berkeleyvision.org (Caffe)

    • https://github.com/MarvinTeichmann/tensorflow-fcn (Tensorflow)

    • https://github.com/aurora95/Keras-FCN (Keras)

    • https://github.com/mzaradzki/neuralnets/tree/master/vgg_segmentation_keras (Keras)

    • https://github.com/k3nt0w/FCN_via_keras (Keras)

    • https://github.com/shekkizh/FCN.tensorflow (Tensorflow)

    • https://github.com/seewalker/tf-pixelwise (Tensorflow)

    • https://github.com/divamgupta/image-segmentation-keras (Keras)

    • https://github.com/ZijunDeng/pytorch-semantic-segmentation (PyTorch)

    • https://github.com/wkentaro/pytorch-fcn (PyTorch)

    • https://github.com/wkentaro/fcn (Chainer)

    • https://github.com/apache/incubator-mxnet/tree/master/example/fcn-xs (MxNet)

    • https://github.com/muyang0320/tf-fcn (Tensorflow)

    • https://github.com/ycszen/pytorch-seg (PyTorch)

    • https://github.com/Kaixhin/FCN-semantic-segmentation (PyTorch)

  5. ENet (https://arxiv.org/pdf/1606.02147.pdf)

    • https://github.com/TimoSaemann/ENet (Caffe)

    • https://github.com/e-lab/ENet-training (Torch)

    • https://github.com/PavlosMelissinos/enet-keras (Keras)

  6. LinkNet (https://arxiv.org/pdf/1707.03718.pdf)

    • https://github.com/e-lab/LinkNet (Torch)

  7. DenseNet (https://arxiv.org/pdf/1608.06993.pdf)

    • https://github.com/flyyufelix/DenseNet-Keras (Keras)

  8. Tiramisu (https://arxiv.org/pdf/1611.09326.pdf)

    • https://github.com/0bserver07/One-Hundred-Layers-Tiramisu (Keras)

    • https://github.com/SimJeg/FC-DenseNet (Lasagne)

  9. DilatedNet (https://arxiv.org/pdf/1511.07122.pdf)

    • https://github.com/nicolov/segmentation_keras (Keras)

  10. PixelNet (https://arxiv.org/pdf/1609.06694.pdf)

    • https://github.com/aayushbansal/PixelNet (Caffe)

  11. ICNet (https://arxiv.org/pdf/1704.08545.pdf)

    • https://github.com/hszhao/ICNet (Caffe)

  12. ERFNet (http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf)

    • https://github.com/Eromera/erfnet (Torch)

  13. RefineNet (https://arxiv.org/pdf/1611.06612.pdf)

    • https://github.com/guosheng/refinenet (MatConvNet)

  14. PSPNet (https://arxiv.org/pdf/1612.01105.pdf)

    • https://github.com/hszhao/PSPNet (Caffe)

    • https://github.com/ZijunDeng/pytorch-semantic-segmentation (PyTorch)

    • https://github.com/mitmul/chainer-pspnet (Chainer)

    • https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow (Keras/Tensorflow)

    • https://github.com/pudae/tensorflow-pspnet (Tensorflow)

  15. CRFasRNN (http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf)

    • https://github.com/torrvision/crfasrnn (Caffe)

    • https://github.com/sadeepj/crfasrnn_keras (Keras)

  16. Dilated convolution (https://arxiv.org/pdf/1511.07122.pdf)

    • https://github.com/fyu/dilation (Caffe)

    • https://github.com/fyu/drn#semantic-image-segmentataion (PyTorch)

    • https://github.com/hangzhaomit/semantic-segmentation-pytorch (PyTorch)

  17. DeconvNet (https://arxiv.org/pdf/1505.04366.pdf)

    • http://cvlab.postech.ac.kr/research/deconvnet/ (Caffe)

    • https://github.com/HyeonwooNoh/DeconvNet (Caffe)

    • https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation (Tensorflow)

  18. FRRN (https://arxiv.org/pdf/1611.08323.pdf)

    • https://github.com/TobyPDE/FRRN (Lasagne)

  19. GCN (https://arxiv.org/pdf/1703.02719.pdf)

    • https://github.com/ZijunDeng/pytorch-semantic-segmentation (PyTorch)

    • https://github.com/ycszen/pytorch-seg (PyTorch)

  20. DUC, HDC (https://arxiv.org/pdf/1702.08502.pdf)

    • https://github.com/ZijunDeng/pytorch-semantic-segmentation (PyTorch)

    • https://github.com/ycszen/pytorch-seg (PyTorch)

  21. Segaware (https://arxiv.org/pdf/1708.04607.pdf)

    • https://github.com/aharley/segaware (Caffe)

  22. Semantic Segmentation using Adversarial Networks (https://arxiv.org/pdf/1611.08408.pdf)

    • https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks (Chainer)


Instance aware segmentation

  1. FCIS [https://arxiv.org/pdf/1611.07709.pdf]

    • https://github.com/msracver/FCIS [MxNet]

  2. MNC [https://arxiv.org/pdf/1512.04412.pdf]

    • https://github.com/daijifeng001/MNC [Caffe]

  3. DeepMask [https://arxiv.org/pdf/1506.06204.pdf]

    • https://github.com/facebookresearch/deepmask [Torch]

  4. SharpMask [https://arxiv.org/pdf/1603.08695.pdf]

    • https://github.com/facebookresearch/deepmask [Torch]

  5. Mask-RCNN [https://arxiv.org/pdf/1703.06870.pdf]

    • https://github.com/CharlesShang/FastMaskRCNN [Tensorflow]

    • https://github.com/TuSimple/mx-maskrcnn [MxNet]

    • https://github.com/matterport/Mask_RCNN [Keras]

    1. https://github.com/jasjeetIM/Mask-RCNN [Caffe]

  6. RIS [https://arxiv.org/pdf/1511.08250.pdf]

    • https://github.com/bernard24/RIS [Torch]

  7. FastMask [https://arxiv.org/pdf/1612.08843.pdf]

    • https://github.com/voidrank/FastMask [Caffe]


Satellite images segmentation

  • https://github.com/mshivaprakash/sat-seg-thesis

  • https://github.com/KGPML/Hyperspectral

  • https://github.com/lopuhin/kaggle-dstl

  • https://github.com/mitmul/ssai

  • https://github.com/mitmul/ssai-cnn

  • https://github.com/azavea/raster-vision

  • https://github.com/nshaud/DeepNetsForEO

  • https://github.com/trailbehind/DeepOSM


Video segmentation

  • https://github.com/shelhamer/clockwork-fcn

  • https://github.com/JingchunCheng/Seg-with-SPN


Autonomous driving

  • https://github.com/MarvinTeichmann/MultiNet

  • https://github.com/MarvinTeichmann/KittiSeg

  • https://github.com/vxy10/p5_VehicleDetection_Unet [Keras]

  • https://github.com/ndrplz/self-driving-car

  • https://github.com/mvirgo/MLND-Capstone


Annotation Tools:

  • https://github.com/AKSHAYUBHAT/ImageSegmentation

  • https://github.com/kyamagu/js-segment-annotator

  • https://github.com/CSAILVision/LabelMeAnnotationTool

  • https://github.com/seanbell/opensurfaces-segmentation-ui

  • https://github.com/lzx1413/labelImgPlus

  • https://github.com/wkentaro/labelme


Datasets

  1. Stanford Background Dataset[http://dags.stanford.edu/projects/scenedataset.html]

    1. Sift Flow Dataset[http://people.csail.mit.edu/celiu/SIFTflow/]

    2. Barcelona Dataset[http://www.cs.unc.edu/~jtighe/Papers/ECCV10/]

    3. Microsoft COCO dataset[http://mscoco.org/]

    4. MSRC Dataset[http://research.microsoft.com/en-us/projects/objectclassrecognition/]

    5. LITS Liver Tumor Segmentation Dataset[https://competitions.codalab.org/competitions/15595]

    6. KITTI[http://www.cvlibs.net/datasets/kitti/eval_road.php]

    7. Stanford background dataset[http://dags.stanford.edu/projects/scenedataset.html]

    8. Data from Games dataset[https://download.visinf.tu-darmstadt.de/data/from_games/]

    9. Human parsing dataset[https://github.com/lemondan/HumanParsing-Dataset]

    10. Silenko person database[https://github.com/Maxfashko/CamVid]

    11. Mapillary Vistas Dataset[https://www.mapillary.com/dataset/vistas]

    12. Microsoft AirSim[https://github.com/Microsoft/AirSim]

    13. MIT Scene Parsing Benchmark[http://sceneparsing.csail.mit.edu/]

    14. COCO 2017 Stuff Segmentation Challenge[http://cocodataset.org/#stuff-challenge2017]

    15. ADE20K Dataset[http://groups.csail.mit.edu/vision/datasets/ADE20K/]

    16. INRIA Annotations for Graz-02[http://lear.inrialpes.fr/people/marszalek/data/ig02/]


比赛

  1. MSRC-21 [http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html]

  2. Cityscapes [https://www.cityscapes-dataset.com/benchmarks/]

  3. VOC2012 [http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6]


领域专家

  1. Jonathan Long

    • [http://people.eecs.berkeley.edu/~jonlong/\]

  2. Liang-Chieh Chen

    • [http://liangchiehchen.com/]

  3. Hyeonwoo Noh

    • [http://cvlab.postech.ac.kr/~hyeonwoonoh/\]

  4. Bharath Hariharan

    • [http://home.bharathh.info/]

  5. Fisher Yu

    • [http://www.yf.io/]

  6. Vijay Badrinarayanan

    • [https://sites.google.com/site/vijaybacademichomepage/home/papers]

  7. Guosheng Lin

    • [https://sites.google.com/site/guoshenglin/]


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