semantic-segmentation.md

Semantic segmentation

• SemanticSegmentation_DL
• U-Net [https://arxiv.org/pdf/1505.04597.pdf] [2015]
  • https://github.com/zhixuhao/unet [Keras]
  • 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]
  • https://github.com/masahi/nnvm-vision-demo/blob/master/unet_segmentation.py [onnx+nnvm]
  • https://github.com/qureai/ultrasound-nerve-segmentation-using-torchnet [Torch]
  • https://github.com/ternaus/TernausNet [PyTorch]
• SegNet [https://arxiv.org/pdf/1511.00561.pdf] [2016]
  • 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]
• DeepLab [https://arxiv.org/pdf/1606.00915.pdf] [2017]
  • 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]
  • https://github.com/bonlime/keras-deeplab-v3-plus [Keras]
• FCN [https://arxiv.org/pdf/1605.06211.pdf] [2016]
  • 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]
  • https://github.com/petrama/VGGSegmentation [Tensorflow]
  • https://github.com/simonguist/testing-fcn-for-cityscapes [Caffe]
  • https://github.com/hellochick/semantic-segmentation-tensorflow [Tensorflow]
  • https://github.com/pierluigiferrari/fcn8s_tensorflow [Tensorflow]
• ENet [https://arxiv.org/pdf/1606.02147.pdf] [2016]
  • https://github.com/TimoSaemann/ENet [Caffe]
  • https://github.com/e-lab/ENet-training [Torch]
  • https://github.com/PavlosMelissinos/enet-keras [Keras]
  • https://github.com/fregu856/segmentation [Tensorflow]
  • https://github.com/kwotsin/TensorFlow-ENet [Tensorflow]
  • https://github.com/davidtvs/PyTorch-ENet [PyTorch]
• LinkNet [https://arxiv.org/pdf/1707.03718.pdf] [2017]
  • https://github.com/e-lab/LinkNet [Torch]
• DenseNet [https://arxiv.org/pdf/1608.06993.pdf] [2018]
  • https://github.com/flyyufelix/DenseNet-Keras [Keras]
• Tiramisu [https://arxiv.org/pdf/1611.09326.pdf] [2017]
  • https://github.com/0bserver07/One-Hundred-Layers-Tiramisu [Keras]
  • https://github.com/SimJeg/FC-DenseNet [Lasagne]
• DilatedNet [https://arxiv.org/pdf/1511.07122.pdf] [2016]
  • https://github.com/nicolov/segmentation_keras [Keras]
  • https://github.com/fyu/dilation [Caffe]
  • https://github.com/fyu/drn#semantic-image-segmentataion [PyTorch]
  • https://github.com/hangzhaomit/semantic-segmentation-pytorch [PyTorch]
• PixelNet [https://arxiv.org/pdf/1609.06694.pdf] [2016]
  • https://github.com/aayushbansal/PixelNet [Caffe]
• ICNet [https://arxiv.org/pdf/1704.08545.pdf] [2017]
  • https://github.com/hszhao/ICNet [Caffe]
  • https://github.com/ai-tor/Keras-ICNet [Keras]
  • https://github.com/hellochick/ICNet-tensorflow [Tensorflow]
• ERFNet [http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf] [?]
  • https://github.com/Eromera/erfnet [Torch]
  • https://github.com/Eromera/erfnet_pytorch [PyTorch]
• RefineNet [https://arxiv.org/pdf/1611.06612.pdf] [2016]
  • https://github.com/guosheng/refinenet [MatConvNet]
• PSPNet [https://arxiv.org/pdf/1612.01105.pdf,https://hszhao.github.io/projects/pspnet/] [2017]
  • 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]
  • https://github.com/hellochick/PSPNet-tensorflow [Tensorflow]
  • https://github.com/hellochick/semantic-segmentation-tensorflow [Tensorflow]
• DeconvNet [https://arxiv.org/pdf/1505.04366.pdf] [2015]
  • http://cvlab.postech.ac.kr/research/deconvnet/ [Caffe]
  • https://github.com/HyeonwooNoh/DeconvNet [Caffe]
  • https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation [Tensorflow]
• FRRN [https://arxiv.org/pdf/1611.08323.pdf] [2016]
  • https://github.com/TobyPDE/FRRN [Lasagne]
• GCN [https://arxiv.org/pdf/1703.02719.pdf] [2017]
  • https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]
  • https://github.com/ycszen/pytorch-seg [PyTorch]
• LRR [https://arxiv.org/pdf/1605.02264.pdf] [2016]
  • https://github.com/golnazghiasi/LRR [Matconvnet]
• DUC, HDC [https://arxiv.org/pdf/1702.08502.pdf] [2017]
  • https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]
  • https://github.com/ycszen/pytorch-seg [PyTorch]
• MultiNet [https://arxiv.org/pdf/1612.07695.pdf] [2016]
  • https://github.com/MarvinTeichmann/MultiNet
  • https://github.com/MarvinTeichmann/KittiSeg
• Segaware [https://arxiv.org/pdf/1708.04607.pdf] [2017]
  • https://github.com/aharley/segaware [Caffe]
• Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf] [2016]
  • https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks [Chainer]
• PixelDCN [https://arxiv.org/pdf/1705.06820.pdf] [2017]
  • https://github.com/HongyangGao/PixelDCN [Tensorflow]
• ShuffleSeg [https://arxiv.org/pdf/1803.03816.pdf] [2018]
  • https://github.com/MSiam/TFSegmentation [TensorFlow]
• AdaptSegNet [https://arxiv.org/pdf/1802.10349.pdf] [2018]
  • https://github.com/wasidennis/AdaptSegNet [PyTorch]
• TuSimple-DUC [https://arxiv.org/pdf/1702.08502.pdf] [2018]
  • https://github.com/TuSimple/TuSimple-DUC [MxNet]

Instance aware segmentation

• FCIS [https://arxiv.org/pdf/1611.07709.pdf]
  • https://github.com/msracver/FCIS [MxNet]
• MNC [https://arxiv.org/pdf/1512.04412.pdf]
  • https://github.com/daijifeng001/MNC [Caffe]
• DeepMask [https://arxiv.org/pdf/1506.06204.pdf]
  • https://github.com/facebookresearch/deepmask [Torch]
• SharpMask [https://arxiv.org/pdf/1603.08695.pdf]
  • https://github.com/facebookresearch/deepmask [Torch]
• Mask-RCNN [https://arxiv.org/pdf/1703.06870.pdf]
  • https://github.com/CharlesShang/FastMaskRCNN [Tensorflow]
  • https://github.com/jasjeetIM/Mask-RCNN [Caffe]
  • https://github.com/TuSimple/mx-maskrcnn [MxNet]
  • https://github.com/matterport/Mask_RCNN [Keras]
• RIS [https://arxiv.org/pdf/1511.08250.pdf]
  • https://github.com/bernard24/RIS [Torch]
• FastMask [https://arxiv.org/pdf/1612.08843.pdf]
  • https://github.com/voidrank/FastMask [Caffe]
• BlitzNet [https://arxiv.org/pdf/1708.02813.pdf]
  • https://github.com/dvornikita/blitznet [Tensorflow]
• PANet [https://arxiv.org/pdf/1803.01534.pdf] [2018]
  • https://github.com/ShuLiu1993/PANet [Caffe]

Weakly-supervised segmentation

• SEC [https://arxiv.org/pdf/1603.06098.pdf]
  • https://github.com/kolesman/SEC [Caffe]

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Semantic Segmentation

• PASCAL VOC2012 Challenge Leaderboard (01 Sep. 2016) (from PASCAL VOC2012 leaderboards)
• Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016. [Paper] [Code]*
• Efficient piecewise training of deep structured models for semantic segmentation. [Paper] (1st ranked in VOC2012)
• Deeply Learning the Messages in Message Passing Inference. [Paper] (4th ranked in VOC2012)
• Semantic Image Segmentation via Deep Parsing Network. ICCV 2015 [Paper] (2nd ranked in VOC 2012)
• Surpassing Humans in Boundary Detection using Deep Learning (4th ranked in VOC 2012) [Paper]
• BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. [Paper] (6th ranked in VOC2012)
• Learning Deconvolution Network for Semantic Segmentation. [Paper] (7th ranked in VOC2012)
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation. [Paper]
• Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, [Paper] [Project Page]
• Conditional Random Fields as Recurrent Neural Networks. [Paper] (8th ranked in VOC2012)
• Weakly-and semi-supervised learning of a DCNN for semantic image segmentation. [Paper] (9th ranked in VOC2012)
• Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015 [Paper]
• Joint Calibration [Paper]
• Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015. [Paper-CVPR15] [Paper-arXiv15]
• Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015. [Paper]
• Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. [Paper]
• Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012. [Paper-ICML12]
• Learning Hierarchical Features for Scene Labeling, PAMI, 2013. [Paper-PAMI13]
• "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." 2015. [Paper]
• "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." 2015. [Paper]
• "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [Paper]
• "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, [Paper]
• "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, [Paper]
• "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [Paper]

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