图像语义分割最新文章汇总（附论文链接和公开代码）

呐，我也是做图像分割的啦，最近看到有大佬整理了图像分割方面最新的论文，觉得很有帮助，就转载过来了，感觉又有很多要学的内容了。

 

Semantic Segmentation

1. Adaptive Affinity Field for Semantic Segmentation – ECCV2018 [Paper] [HomePage]
2. Pyramid Attention Network for Semantic Segmentation – 2018 – Face++ [Paper]
3. Autofocus Layer for Semantic Segmentation – 2018 [Paper [Code-PyTorch]
4. ExFuse: Enhancing Feature Fusion for Semantic Segmentation – 2018 – Face++ [Paper]
5. DifNet: Semantic Segmentation by Diffusion Networks – 2018 [Paper]
6. Convolutional CRFs for Semantic Segmentation – 2018 [Paper][Code-PyTorch]
7. ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time – 2018 [Paper]
8. Learning a Discriminative Feature Network for Semantic Segmentation – CVPR2018 – Face++ [Paper]
9. Vortex Pooling: Improving Context Representation in Semantic Segmentation – 2018[Paper]
10. Fully Convolutional Adaptation Networks for Semantic Segmentation – CVPR2018 [Paper]
11. A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation – 2018 [Paper]
12. Context Encoding for Semantic Segmentation – 2018 [Paper] [Code-PyTorch]
13. ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation – 2018 [Paper]
14. Dynamic-structured Semantic Propagation Network – 2018 – CMU [Paper]
15. ShuffleSeg: Real-time Semantic Segmentation Network-2018 [Paper] [Code-TensorFlow]
16. RTSeg: Real-time Semantic Segmentation Comparative Study – 2018 [Paper] [Code-TensorFlow]
17. Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation – 2018 [Paper]
18. DeepLabV3+:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation – 2018 – Google [Paper] [Code-Tensorflow] [Code-Karas]
19. Adversarial Learning for Semi-Supervised Semantic Segmentation – 2018 [Paper] [Code-PyTorch]
20. Locally Adaptive Learning Loss for Semantic Image Segmentation – 2018 [Paper]
21. Learning to Adapt Structured Output Space for Semantic Segmentation – 2018 [Paper]
22. Improved Image Segmentation via Cost Minimization of Multiple Hypotheses – 2018[Paper] [Code-Matlab]
23. TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation – 2018 – Kaggle [Paper] [Code-PyTorch] [Kaggle-Carvana Image Masking Challenge]
24. Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation – 2018 – Google [Paper]
25. End-to-end Detection-Segmentation Network With ROI Convolution – 2018 [Paper]
26. Mix-and-Match Tuning for Self-Supervised Semantic Segmentation – AAAI2018 [Project] [Paper] [Code-Caffe]
27. Learning to Segment Every Thing-2017 [Paper] [Code-Caffe2] [Code-PyTorch]
28. Deep Dual Learning for Semantic Image Segmentation-2017 [Paper]
29. Scene Parsing with Global Context Embedding – 2017 – ICCV [Paper]
30. FoveaNet: Perspective-aware Urban Scene Parsing – 2017 – ICCV [Paper]
31. Segmentation-Aware Convolutional Networks Using Local Attention Masks – 2017 [Paper] [Code-Caffe] [Project]
32. Stacked Deconvolutional Network for Semantic Segmentation-2017 [Paper]
33. Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF – CVPR2017 [Paper] [Caffe-Code]
34. BlitzNet: A Real-Time Deep Network for Scene Understanding-2017 [Project] [Code-Tensorflow] [Paper]
35. Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation -2017[Paper] [Code-Caffe]
36. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation – 2017 [Paper] [Code-Torch]
37. Rethinking Atrous Convolution for Semantic Image Segmentation-2017(DeeplabV3)[Paper]
38. Learning Object Interactions and Descriptions for Semantic Image Segmentation-2017 [Paper]
39. Pixel Deconvolutional Networks-2017 [Code-Tensorflow] [Paper]
40. Dilated Residual Networks-2017 [Paper] [Code-PyTorch]
41. Recurrent Scene Parsing with Perspective Understanding in the Loop – 2017 [Project][Paper] [Code-MatConvNet]
42. A Review on Deep Learning Techniques Applied to Semantic Segmentation-2017 [Paper]
43. BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks [Paper]
44. Efficient ConvNet for Real-time Semantic Segmentation – 2017 [Paper]
45. ICNet for Real-Time Semantic Segmentation on High-Resolution Images-2017 [Project] [Code] [Paper] [Video]
46. Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade-2017 [Paper] [Poster] [Project] [Code-Caffe] [Slides]
47. Loss Max-Pooling for Semantic Image Segmentation-2017 [Paper]
48. Annotating Object Instances with a Polygon-RNN-2017 [Project] [Paper]
49. Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation-2017 [Project] [Code-Torch7]
50. Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation-2017 [Paper]
51. Adversarial Examples for Semantic Image Segmentation-2017 [Paper]
52. Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network-2017 [Paper]
53. Label Refinement Network for Coarse-to-Fine Semantic Segmentation-2017 [Paper]
54. PixelNet: Representation of the pixels, by the pixels, and for the pixels-2017 [Project] [Code-Caffe] [Paper]
55. LabelBank: Revisiting Global Perspectives for Semantic Segmentation-2017 [Paper]
56. Progressively Diffused Networks for Semantic Image Segmentation-2017 [Paper]
57. Understanding Convolution for Semantic Segmentation-2017 [Model-Mxnet] [Mxnet-Code] [Paper]
58. Predicting Deeper into the Future of Semantic Segmentation-2017 [Paper]
59. Pyramid Scene Parsing Network-2017 [Project] [Code-Caffe] [Paper] [Slides]
60. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation-2016 [Paper]
61. FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics-2016 [Code-PyTorch] [Paper]
62. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation-2016 [Code-MatConvNet] [Paper]
63. Learning from Weak and Noisy Labels for Semantic Segmentation – 2017 [Paper]
64. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation [Code-Theano] [Code-Keras1] [Code-Keras2] [Paper]
65. Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes [Code-Theano] [Paper]
66. PixelNet: Towards a General Pixel-level Architecture-2016 [Paper]
67. Recalling Holistic Information for Semantic Segmentation-2016 [Paper]
68. Semantic Segmentation using Adversarial Networks-2016 [Paper] [Code-Chainer]
69. Region-based semantic segmentation with end-to-end training-2016 [Paper]
70. Exploring Context with Deep Structured models for Semantic Segmentation-2016 [Paper]
71. Better Image Segmentation by Exploiting Dense Semantic Predictions-2016 [Paper]
72. Boundary-aware Instance Segmentation-2016 [Paper]
73. Improving Fully Convolution Network for Semantic Segmentation-2016 [Paper]
74. Deep Structured Features for Semantic Segmentation-2016 [Paper]
75. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs-2016 [Project] [Code-Caffe] [Code-Tensorflow] [Code-PyTorch] [Paper]
76. DeepLab: Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs-2014 [Code-Caffe1] [Code-Caffe2] [Paper]
77. Deep Learning Markov Random Field for Semantic Segmentation-2016 [Project] [Paper]
78. Convolutional Random Walk Networks for Semantic Image Segmentation-2016 [Paper]
79. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016 [Code-Caffe1] [Code-Caffe2] [Paper] [Blog]
80. High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks-2016 [Paper]
81. ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation-2016 [Paper]
82. Object Boundary Guided Semantic Segmentation-2016 [Code-Caffe] [Paper]
83. Segmentation from Natural Language Expressions-2016 [Project] [Code-Tensorflow][Code-Caffe] [Paper]
84. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation-2016 [Code-Caffe] [Paper]
85. Global Deconvolutional Networks for Semantic Segmentation-2016 [Paper] [Code-Caffe]
86. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network-2015 [Project] [Code-Caffe] [Paper]
87. Learning Dense Convolutional Embeddings for Semantic Segmentation-2015 [Paper]
88. ParseNet: Looking Wider to See Better-2015 [Code-Caffe] [Model-Caffe] [Paper]
89. Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation-2015[Project] [Code-Caffe] [Paper]
90. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation-2015 [Project] [Code-Caffe] [Paper] [Tutorial1] [Tutorial2]
91. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling-2015 [Code-Caffe] [Code-Chainer] [Paper]
92. Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform-2015 [Paper]
93. Semantic Segmentation with Boundary Neural Fields-2015 [Code] [Paper]
94. Semantic Image Segmentation via Deep Parsing Network-2015 [Project] [Paper1] [Paper2] [Slides]
95. What’s the Point: Semantic Segmentation with Point Supervision-2015 [Project] [Code-Caffe] [Model-Caffe] [Paper]
96. U-Net: Convolutional Networks for Biomedical Image Segmentation-2015 [Project] [Code+Data] [Code-Keras] [Code-Tensorflow] [Paper] [Notes]
97. Learning Deconvolution Network for Semantic Segmentation(DeconvNet)-2015 [Project] [Code-Caffe] [Paper] [Slides]
98. Multi-scale Context Aggregation by Dilated Convolutions-2015 [Project] [Code-Caffe][Code-Keras] [Paper] [Notes]
99. ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation-2015 [Code-Theano] [Paper]
100. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation-2015 [Paper]
101. Feedforward semantic segmentation with zoom-out features-2015 [Code] [Paper] [Video]
102. Conditional Random Fields as Recurrent Neural Networks-2015 [Project] [Code-Caffe1] [Code-Caffe2] [Demo] [Paper1] [Paper2]
103. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation-2015 [Paper]
104. Fully Convolutional Networks for Semantic Segmentation-2015 [Code-Caffe] [Model-Caffe] [Code-Tensorflow1] [Code-Tensorflow2] [Code-Chainer] [Code-PyTorch] [Paper1] [Paper2] [Slides1] [Slides2]
105. Deep Joint Task Learning for Generic Object Extraction-2014 [Project] [Code-Caffe] [Dataset] [Paper]
106. 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]

 

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