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Semantic Segmentation

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

Panoptic Segmentation

1. Panoptic Segmentation – 2018 

Human Parsing

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

Clothes Parsing

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

Instance Segmentation

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

Segment Object Candidates

1. FastMask: Segment Object Multi-scale Candidates in One Shot-2016  
2. Learning to Refine Object Segments-2016  
3. Learning to Segment Object Candidates-2015   

Foreground Object Segmentation

1. Pixel Objectness-2017   
2. A Deep Convolutional Neural Network for Background Subtraction-2017 

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