September 15, 2018 • Read: 321831 • 语义分割阅读设置


2.截止 2018.09.15 尚未对本内容进行优化和筛选,后续对有针对性的筛选和整理,尤其在代码复现和论文理解方面。

Semantic Segmentation

  • Adaptive Affinity Field for Semantic Segmentation – ECCV2018 [Paper] [HomePage]

  • Pyramid Attention Network for Semantic Segmentation – 2018 – Face++ [Paper]

  • Autofocus Layer for Semantic Segmentation – 2018 [Paper [Code-PyTorch]

  • ExFuse: Enhancing Feature Fusion for Semantic Segmentation – 2018 – Face++ [Paper]

  • DifNet: Semantic Segmentation by Diffusion Networks – 2018 [Paper]

  • Convolutional CRFs for Semantic Segmentation – 2018 [Paper][Code-PyTorch]

  • ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time – 2018 [Paper]

  • Learning a Discriminative Feature Network for Semantic Segmentation – CVPR2018 – Face++ [Paper]

  • Vortex Pooling: Improving Context Representation in Semantic Segmentation – 2018 [Paper]

  • Fully Convolutional Adaptation Networks for Semantic Segmentation – CVPR2018 [Paper]

  • A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation – 2018 [Paper]

  • Context Encoding for Semantic Segmentation – 2018 [Paper] [Code-PyTorch]

  • ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation – 2018 [Paper]

  • Dynamic-structured Semantic Propagation Network – 2018 – CMU [Paper]

  • ShuffleSeg: Real-time Semantic Segmentation Network-2018 [Paper] [Code-TensorFlow]

  • RTSeg: Real-time Semantic Segmentation Comparative Study – 2018 [Paper] [Code-TensorFlow]

  • Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation – 2018 [Paper]

  • DeepLabV3+:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation – 2018 – Google [Paper] [Code-Tensorflow] [Code-Karas]

  • Adversarial Learning for Semi-Supervised Semantic Segmentation – 2018 [Paper] [Code-PyTorch]

  • Locally Adaptive Learning Loss for Semantic Image Segmentation – 2018 [Paper]

  • Learning to Adapt Structured Output Space for Semantic Segmentation – 2018 [Paper]

  • Improved Image Segmentation via Cost Minimization of Multiple Hypotheses – 2018 [Paper] [Code-Matlab]

  • TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation – 2018 – Kaggle [Paper] [Code-PyTorch] [Kaggle-Carvana Image Masking Challenge]

  • Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation – 2018 – Google [Paper]

  • End-to-end Detection-Segmentation Network With ROI Convolution – 2018 [Paper]

  • Mix-and-Match Tuning for Self-Supervised Semantic Segmentation – AAAI2018 [Project] [Paper] [Code-Caffe]

  • Learning to Segment Every Thing-2017 [Paper] [Code-Caffe2] [Code-PyTorch]

  • Deep Dual Learning for Semantic Image Segmentation-2017 [Paper]

  • Scene Parsing with Global Context Embedding – 2017 – ICCV [Paper]

  • FoveaNet: Perspective-aware Urban Scene Parsing – 2017 – ICCV [Paper]

  • Segmentation-Aware Convolutional Networks Using Local Attention Masks – 2017 [Paper] [Code-Caffe] [Project]

  • Stacked Deconvolutional Network for Semantic Segmentation-2017 [Paper]

  • Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF – CVPR2017 [Paper] [Caffe-Code]

  • BlitzNet: A Real-Time Deep Network for Scene Understanding-2017 [Project] [Code-Tensorflow] [Paper]

  • Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation -2017 [Paper] [Code-Caffe]

  • LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation – 2017 [Paper] [Code-Torch]

  • Rethinking Atrous Convolution for Semantic Image Segmentation-2017(DeeplabV3) [Paper]

  • Learning Object Interactions and Descriptions for Semantic Image Segmentation-2017 [Paper]

  • Pixel Deconvolutional Networks-2017 [Code-Tensorflow] [Paper]

  • Dilated Residual Networks-2017 [Paper] [Code-PyTorch]

  • Recurrent Scene Parsing with Perspective Understanding in the Loop – 2017 [Project] [Paper] [Code-MatConvNet]

  • A Review on Deep Learning Techniques Applied to Semantic Segmentation-2017 [Paper]

  • BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks [Paper]

  • Efficient ConvNet for Real-time Semantic Segmentation – 2017 [Paper]

  • ICNet for Real-Time Semantic Segmentation on High-Resolution Images-2017 [Project] [Code] [Paper] [Video]

  • Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade-2017 [Paper] [Poster] [Project] [Code-Caffe] [Slides]

  • Loss Max-Pooling for Semantic Image Segmentation-2017 [Paper]

  • Annotating Object Instances with a Polygon-RNN-2017 [Project] [Paper]

  • Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation-2017 [Project] [Code-Torch7]

  • Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation-2017 [Paper]

  • Adversarial Examples for Semantic Image Segmentation-2017 [Paper]

  • Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network-2017 [Paper]

  • Label Refinement Network for Coarse-to-Fine Semantic Segmentation-2017 [Paper]

  • PixelNet: Representation of the pixels, by the pixels, and for the pixels-2017 [Project] [Code-Caffe] [Paper]

  • LabelBank: Revisiting Global Perspectives for Semantic Segmentation-2017 [Paper]

  • Progressively Diffused Networks for Semantic Image Segmentation-2017 [Paper]

  • Understanding Convolution for Semantic Segmentation-2017 [Model-Mxnet] [Mxnet-Code] [Paper]

  • Predicting Deeper into the Future of Semantic Segmentation-2017 [Paper]

  • Pyramid Scene Parsing Network-2017 [Project] [Code-Caffe] [Paper] [Slides]

  • FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation-2016 [Paper]

  • FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics-2016 [Code-PyTorch] [Paper]

  • RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation-2016 [Code-MatConvNet] [Paper]

  • Learning from Weak and Noisy Labels for Semantic Segmentation – 2017 [Paper]

  • The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation [Code-Theano] [Code-Keras1] [Code-Keras2] [Paper]

  • Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes [Code-Theano] [Paper]

  • PixelNet: Towards a General Pixel-level Architecture-2016 [Paper]

  • Recalling Holistic Information for Semantic Segmentation-2016 [Paper]

  • Semantic Segmentation using Adversarial Networks-2016 [Paper] [Code-Chainer]

  • Region-based semantic segmentation with end-to-end training-2016 [Paper]

  • Exploring Context with Deep Structured models for Semantic Segmentation-2016 [Paper]

  • Better Image Segmentation by Exploiting Dense Semantic Predictions-2016 [Paper]

  • Boundary-aware Instance Segmentation-2016 [Paper]

  • Improving Fully Convolution Network for Semantic Segmentation-2016 [Paper]

  • Deep Structured Features for Semantic Segmentation-2016 [Paper]

  • DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs-2016 [Project] [Code-Caffe] [Code-Tensorflow] [Code-PyTorch] [Paper]

  • DeepLab: Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs-2014 [Code-Caffe1] [Code-Caffe2] [Paper]

  • Deep Learning Markov Random Field for Semantic Segmentation-2016 [Project] [Paper]

  • Convolutional Random Walk Networks for Semantic Image Segmentation-2016 [Paper]

  • ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016 [Code-Caffe1] [Code-Caffe2] [Paper] [Blog]

  • High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks-2016 [Paper]

  • ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation-2016 [Paper]

  • Object Boundary Guided Semantic Segmentation-2016 [Code-Caffe] [Paper]

  • Segmentation from Natural Language Expressions-2016 [Project] [Code-Tensorflow] [Code-Caffe] [Paper]

  • Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation-2016 [Code-Caffe] [Paper]

  • Global Deconvolutional Networks for Semantic Segmentation-2016 [Paper] [Code-Caffe]

  • Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network-2015 [Project] [Code-Caffe] [Paper]

  • Learning Dense Convolutional Embeddings for Semantic Segmentation-2015 [Paper]

  • ParseNet: Looking Wider to See Better-2015 [Code-Caffe] [Model-Caffe] [Paper]

  • Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation-2015 [Project] [Code-Caffe] [Paper]

  • SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation-2015 [Project] [Code-Caffe] [Paper] [Tutorial1] [Tutorial2]

  • SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling-2015 [Code-Caffe] [Code-Chainer] [Paper]

  • Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform-2015 [Paper]

  • Semantic Segmentation with Boundary Neural Fields-2015 [Code] [Paper]

  • Semantic Image Segmentation via Deep Parsing Network-2015 [Project] [Paper1] [Paper2] [Slides]

  • What’s the Point: Semantic Segmentation with Point Supervision-2015 [Project] [Code-Caffe] [Model-Caffe] [Paper]

  • U-Net: Convolutional Networks for Biomedical Image Segmentation-2015 [Project] [Code+Data] [Code-Keras] [Code-Tensorflow] [Paper] [Notes]

  • Learning Deconvolution Network for Semantic Segmentation(DeconvNet)-2015 [Project] [Code-Caffe] [Paper] [Slides]

  • Multi-scale Context Aggregation by Dilated Convolutions-2015 [Project] [Code-Caffe] [Code-Keras] [Paper] [Notes]

  • ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation-2015 [Code-Theano] [Paper]

  • BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation-2015 [Paper]

  • Feedforward semantic segmentation with zoom-out features-2015 [Code] [Paper] [Video]

  • Conditional Random Fields as Recurrent Neural Networks-2015 [Project] [Code-Caffe1] [Code-Caffe2] [Demo] [Paper1] [Paper2]

  • Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation-2015 [Paper]

  • Fully Convolutional Networks for Semantic Segmentation-2015 [Code-Caffe] [Model-Caffe] [Code-Tensorflow1] [Code-Tensorflow2] [Code-Chainer] [Code-PyTorch] [Paper1] [Paper2] [Slides1] [Slides2]

  • Deep Joint Task Learning for Generic Object Extraction-2014 [Project] [Code-Caffe] [Dataset] [Paper]

  • Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification-2014 [Code-Caffe] [Paper]

  • Panoptic Segmentation

    1. Panoptic Segmentation – 2018 [Paper]

    Human Parsing

    1. Macro-Micro Adversarial Network for Human Parsing – ECCV2018 [Paper] [Code-PyTorch]
    2. Holistic, Instance-level Human Parsing – 2017 [Paper]
    3. Semi-Supervised Hierarchical Semantic Object Parsing – 2017 [Paper]
    4. Towards Real World Human Parsing: Multiple-Human Parsing in the Wild – 2017 [Paper]
    5. Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing-2017 [Project] [Code-Caffe] [Paper]
    6. Efficient and Robust Deep Networks for Semantic Segmentation – 2017 [Paper] [Project] [Code-Caffe]
    7. Deep Learning for Human Part Discovery in Images-2016 [Code-Chainer] [Paper]
    8. A CNN Cascade for Landmark Guided Semantic Part Segmentation-2016 [Project] [Paper]
    9. Deep Learning for Semantic Part Segmentation With High-level Guidance-2015 [Paper]
    10. Neural Activation Constellations-Unsupervised Part Model Discovery with Convolutional Networks-2015 [Paper]
    11. Human Parsing with Contextualized Convolutional Neural Network-2015 [Paper]
    12. Part detector discovery in deep convolutional neural networks-2014 [Code] [Paper]

    Clothes Parsing

    1. Looking at Outfit to Parse Clothing-2017 [Paper]
    2. Semantic Object Parsing with Local-Global Long Short-Term Memory-2015 [Paper]
    3. A High Performance CRF Model for Clothes Parsing-2014 [Project] [Code] [Dataset] [Paper]
    4. Clothing co-parsing by joint image segmentation and labeling-2013 [Project] [Dataset] [Paper]
    5. Parsing clothing in fashion photographs-2012 [Project] [Paper]

    Instance Segmentation

    1. A Pyramid CNN for Dense-Leaves Segmentation – 2018 [Paper]
    2. Predicting Future Instance Segmentations by Forecasting Convolutional Features – 2018 [Paper]
    3. Path Aggregation Network for Instance Segmentation – CVPR2018 [Paper] [Code-PyTorch]
    4. PixelLink: Detecting Scene Text via Instance Segmentation – AAAI2018 [Code-Tensorflow] [Paper]
    5. MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features – 2017 – google [Paper]
    6. Recurrent Neural Networks for Semantic Instance Segmentation-2017 [Paper]
    7. Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 [Paper]
    8. Semantic Instance Segmentation via Deep Metric Learning-2017 [Paper]
    9. Mask R-CNN-2017 [Code-Tensorflow] [Paper] [Code-Caffe2] [Code-Karas] [Code-PyTorch] [Code-MXNet]
    10. Pose2Instance: Harnessing Keypoints for Person Instance Segmentation-2017 [Paper]
    11. Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 [Paper]
    12. Semantic Instance Segmentation with a Discriminative Loss Function-2017 [Paper]
    13. Fully Convolutional Instance-aware Semantic Segmentation-2016 [Code] [Paper]
    14. End-to-End Instance Segmentation with Recurrent Attention [Paper] [Code-Tensorflow]
    15. Instance-aware Semantic Segmentation via Multi-task Network Cascades-2015 [Code] [Paper]
    16. Recurrent Instance Segmentation-2015 [Project] [Code-Torch7] [Paper] [Poster] [Video]

    Segment Object Candidates

    1. FastMask: Segment Object Multi-scale Candidates in One Shot-2016 [Code-Caffe] [Paper]
    2. Learning to Refine Object Segments-2016 [Code-Torch] [Paper]
    3. Learning to Segment Object Candidates-2015 [Code-Torch] [Code-Theano-Keras] [Paper]

    Foreground Object Segmentation

    1. Pixel Objectness-2017 [Project] [Code-Caffe] [Paper]
    2. A Deep Convolutional Neural Network for Background Subtraction-2017 [Paper]
    Last Modified: January 7, 2019
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