语义分割 - Semantic Segmentation Papers

Author： AIHGF
发布时间：May 17, 2018
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Categories：语义分割

## 1. Semantic Segmentation

1. FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale
   Context Aggregation and Feature Space Super-resolution - 2020 - SenseTime [<Paper>](https://128.84.21.199/pdf/2003.03913.pdf)
2. RGPNet: A Real-Time General Purpose Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1912.01394.pdf)
3. Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1911.12993.pdf)
4. Document Structure Extraction for Forms using Very High Resolution Semantic Segmentation - 2019 - Adobe 文档结构提取 [<Paper>](https://arxiv.org/pdf/1911.12170.pdf)
5. Class-Conditional Domain Adaptation on Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1911.11981.pdf)
6. On Symbiosis of Attribute Prediction and Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1911.11612.pdf)
7. Differentiable Meta-learning Model for Few-shot Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1911.10371.pdf)
8. Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach - 2019 [<Paper>](https://arxiv.org/pdf/1911.08039.pdf)
9. Real-Time Semantic Segmentation via Multiply Spatial Fusion Network - 2019 旷视 [<Paper>](https://arxiv.org/pdf/1911.07217.pdf)
10. Improving Semantic Segmentation of Aerial Images Using Patch-based Attention - 2019 [<Paper>](https://arxiv.org/pdf/1911.08877.pdf)
11. Location-aware Upsampling for Semantic Segmentation - 2019 - CAS [<Paper>](https://arxiv.org/pdf/1911.05250.pdf) [<Code-PyTorch>](https://github.com/HolmesShuan/Location-aware-Upsampling-for-Semantic-Segmentation)
12. Knowledge Distillation for Incremental Learning in Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1911.03462.pdf)
13. Eye Semantic Segmentation with A Lightweight Model - 2019 眼部分割 [<Paper>](https://arxiv.org/pdf/1911.01049.pdf)
14. Distilling Pixel-Wise Feature Similarities for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1910.14226.pdf)
15. PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation - 2019 道路分割 [<Paper>](https://arxiv.org/pdf/1910.13055.pdf) 
16. Multi-source Domain Adaptation for Semantic Segmentation - 2019 - NeurIPS [<Paper>](https://arxiv.org/pdf/1910.12181.pdf) [<Code-Tensorflow>](https://github.com/Luodian/MADAN)
17. Region Mutual Information Loss for Semantic Segmentation - 2019 - NeurIPS [<Paper>](https://arxiv.org/pdf/1910.12037.pdf) [<Code-PyTorch>](https://github.com/ZJULearning/RMI)
18. Correlation Maximized Structural Similarity Loss for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1910.08711.pdf)
19. Deep Semantic Segmentation of Natural and Medical Images: A Review - 2019 医学图像分割综述 [<Paper>](https://arxiv.org/pdf/1910.07655.pdf)
20. CNN-based Semantic Segmentation using Level Set Loss - 2019 KAIST [<Paper>](https://arxiv.org/pdf/1910.00950.pdf)
21. Domain Adaptation for Semantic Segmentation with Maximum Squares Loss - 2019 [<Paper>](https://github.com/ZJULearning/MaxSquareLoss) [<Code-PyTorch>](https://github.com/ZJULearning/MaxSquareLoss)
22. Distributed Iterative Gating Networks for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.12996.pdf)
23. Adaptive Class Weight based Dual Focal Loss for Improved Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.11932.pdf)
24. Object-Contextual Representations for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.11065.pdf)
25. ACFNet: Attentional Class Feature Network for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.09408.pdf)
26. Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation -2019 [<Paper>](https://arxiv.org/pdf/1909.08542.pdf)
27. Feature Pyramid Encoding Network for Real-time Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.08599.pdf)
28. Graph-guided Architecture Search for Real-time Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.06793.pdf)
29. Dual Graph Convolutional Network for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.06121.pdf)
30. Squeeze-and-Attention Networks for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1909.03402.pdf)
31. Semantic Segmentation of Panoramic Images Using a Synthetic Dataset - 2019 [<Paper>](https://arxiv.org/pdf/1909.00532.pdf) [<Code-Github>](https://github.com/Francis515/SYNTHIA-PANO)
32. See More Than Once – Kernel-Sharing Atrous Convolution for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1908.09443.pdf)
33. Feedbackward Decoding for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1908.08584.pdf)
34. Asymmetric Non-local Neural Networks for Semantic Segmentation - 2019 - ICCV [<Paper>](https://arxiv.org/pdf/1908.07678.pdf) [<Code-PyTorch>](https://github.com/MendelXu/ANN)
35. PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment - 2019 [<Paper>](https://arxiv.org/pdf/1908.06391.pdf)
36. Semi-Supervised Semantic Segmentation with High- and Low-level Consistency - 2019 [<Paper>](https://arxiv.org/pdf/1908.05724.pdf)
37. Benchmarking the Robustness of Semantic Segmentation Models - 2019 [<Paper>](https://arxiv.org/pdf/1908.05005.pdf) [<Homepage>](http://vislearn.de/)
38. Distance Map Loss Penalty Term for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1908.03679.pdf)
39. I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1908.02711.pdf)
40. SqueezeNAS: Fast neural architecture search for faster semantic segmentation - 2019 - DeepScale [<Paper>](https://arxiv.org/pdf/1908.01748.pdf)
41. Expectation-Maximization Attention Networks for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.13426.pdf)
42. Incremental Learning Techniques for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.13372.pdf)
43. Interlaced Sparse Self-Attention for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.12273.pdf)
44. DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.12022.pdf)
45. DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.11357.pdf) [<Code-PyTorch>](https://github.com/Reagan1311/DABNet)
46. Cross Attention Network for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.10958.pdf)
47. Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks - 2019 [<Paper>](https://arxiv.org/pdf/1907.09438.pdf)
48. Efficient Segmentation: Learning Downsampling Near Semantic Boundaries - 2019 [<Paper>](https://arxiv.org/pdf/1907.07156.pdf)
49. Improving Semantic Segmentation via Dilated Affinity - 2019 [<Paper>](https://arxiv.org/pdf/1907.07011.pdf)
50. Adaptive Context Encoding Module for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.06082.pdf)
51. A Regularized Convolutional Neural Network for Semantic Image Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1907.05287.pdf)
52. Deep Learning-Based Semantic Segmentation of Microscale Objects - 2019 生物细胞分割 [<Paper>](https://arxiv.org/pdf/1907.03576.pdf)
53. SAN: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images - 2019 高分辨率卫星图像分割 [<Paper>](https://arxiv.org/pdf/1907.03089.pdf)
54. Hard Pixels Mining: Learning Using Privileged Information for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1906.11437.pdf)
55. ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/pdf/1906.09826.pdf)
56. IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things - 2019 [<Paper>](https://arxiv.org/pdf/1906.06597.pdf)
57. Universal Barcode Detector via Semantic Segmentation - 2019 条形码分割 [<Paper>](https://arxiv.org/pdf/1906.06281.pdf)
58. Cross-view Semantic Segmentation for Sensing Surroundings - 2019 [<Paper>](https://arxiv.org/pdf/1906.03560.pdf) [<Code-PyTorch>](https://github.com/pbw-Berwin/View-Parsing-Network) [<Project>](https://view-parsing-network.github.io/)
59. Zero-Shot Semantic Segmentation - 2019 - NeurlPS [<Paper>](https://arxiv.org/pdf/1906.00817.pdf) [<Code-Github>](https://github.com/valeoai/ZS3)
60. Implicit Background Estimation for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1905.13306) [<Code-PyTorch>](https://github.com/olivesgatech/implicit-background-estimation)
61. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation - 2019 - NVIDIA [<Paper>](http://arxiv.org/abs/1907.05740) [<Project>](https://nv-tlabs.github.io/GSCNN/)
62. FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1903.11816) [<Project>](http://wuhuikai.me/FastFCNProject/) [<Code-PyTorch>](https://github.com/wuhuikai/FastFCN) 
63. Structured Knowledge Distillation for Semantic Segmentation - CVPR2019 [<Paper>](https://arxiv.org/abs/1903.04197)
64. Co-Occurrent Features in Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Co-Occurrent_Features_in_Semantic_Segmentation_CVPR_2019_paper.pdf)
65. Semantic Projection Network for Zero- and Few-Label Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xian_Semantic_Projection_Network_for_Zero-_and_Few-Label_Semantic_Segmentation_CVPR_2019_paper.pdf)
66. Context-Reinforced Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhou_Context-Reinforced_Semantic_Segmentation_CVPR_2019_paper.pdf)
67. SwiftNet - In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images - CVPR2019 [<Paper>](https://arxiv.org/abs/1903.08469) [<Code-PyTorch>](https://github.com/orsic/swiftnet)
68. All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_All_About_Structure_Adapting_Structural_Information_Across_Domains_for_Boosting_CVPR_2019_paper.pdf)
69. Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Not_All_Areas_Are_Equal_Transfer_Learning_for_Semantic_Segmentation_CVPR_2019_paper.pdf)
70. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Learning_Semantic_Segmentation_From_Synthetic_Data_A_Geometrically_Guided_Input-Output_CVPR_2019_paper.pdf)
71. Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Song_Box-Driven_Class-Wise_Region_Masking_and_Filling_Rate_Guided_Loss_for_CVPR_2019_paper.pdf)
72. Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.pdf)
73. Geometry-Aware Distillation for Indoor Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Jiao_Geometry-Aware_Distillation_for_Indoor_Semantic_Segmentation_CVPR_2019_paper.pdf)
74. Seamless Scene Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Porzi_Seamless_Scene_Segmentation_CVPR_2019_paper.pdf)
75. ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Vu_ADVENT_Adversarial_Entropy_Minimization_for_Domain_Adaptation_in_Semantic_Segmentation_CVPR_2019_paper.pdf) [<Code-Github>](https://github.com/valeoai/ADVENT)
76. Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation - CVPR2019 [<Paper>](openaccess.thecvf.com/content_CVPR_2019/papers/Luo_Taking_a_Closer_Look_at_Domain_Shift_Category-Level_Adversaries_for_CVPR_2019_paper.pdf)
77. PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Mo_PartNet_A_Large-Scale_Benchmark_for_Fine-Grained_and_Hierarchical_Part-Level_3D_CVPR_2019_paper.pdf) [<Homepage>](https://cs.stanford.edu/~kaichun/partnet/)
78. A Cross-Season Correspondence Dataset for Robust Semantic Segmentation - CVPR2019 [<Paper>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Larsson_A_Cross-Season_Correspondence_Dataset_for_Robust_Semantic_Segmentation_CVPR_2019_paper.pdf) 
79. DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation-Megvii-2019 [<Paper>](https://arxiv.org/abs/1904.02216)
80. DADA: Depth-aware Domain Adaptation in Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1904.01886)
81. GFF: Gated Fully Fusion for Semantic Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1904.01803)
82. DSNet: An Efficient CNN for Road Scene Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1904.05022)
83. FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference - CVPR2019 [<Paper>](https://arxiv.org/abs/1902.10421)
84. An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions - 2019 [<Paper>](https://arxiv.org/abs/1902.07476)
85. Fast-SCNN: Fast Semantic Segmentation Network - 2019 [<Paper>](https://arxiv.org/abs/1902.04502)
86. Data augmentation using learned transforms for one-shot medical image segmentation - CVPR2019 [<Paper>](https://arxiv.org/abs/1902.09383)
87. MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1902.04049)
88. CCNet: Criss-Cross Attention for Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/abs/1811.11721v1) [<Code-PyTorch>](https://github.com/speedinghzl/CCNet) 
89. A PyTorch Semantic Segmentation Toolbox - 2018 [<Paper>](https://weiyc.github.io/assets/pdf/toolbox.pdf) [<Code-PyTorch>](https://github.com/speedinghzl/pytorch-segmentation-toolbox) 
90. ShelfNet for Real-time Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/abs/1811.11254) [<Code-PyTorch>](https://github.com/juntang-zhuang/ShelfNet)
91. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training - ECCV2018 [<Paper>](https://arxiv.org/pdf/1810.07911.pdf)  [<Project>](https://yzou2.github.io/project/self-training/) [<Code-MXNet>](https://github.com/yzou2/cbst)
92. Searching for Efficient Multi-Scale Architectures for Dense Image Prediction - 2018 - Deeplab [<Paper>](https://arxiv.org/abs/1809.04184) [<Code-Deeplab-Tensorflow>](https://github.com/tensorflow/models/tree/master/research/deeplab)
93. Light-Weight RefineNet for Real-Time Semantic Segmentation - bmvc2018 [<Paper>](http://bmvc2018.org/contents/papers/0494.pdf) [<Code-Torch>](https://github.com/DrSleep/light-weight-refinenet)
94. Dual Attention Network for Scene Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1809.02983v1.pdf)
95. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation - ECCV 2018 - Face++ [<Paper>](https://arxiv.org/pdf/1808.00897v1.pdf) [<Code-PyTorch>](https://github.com/ycszen/TorchSeg)
96. Adaptive Affinity Field for Semantic Segmentation - ECCV2018 [<Paper>](https://arxiv.org/pdf/1803.10335.pdf) [<HomePage>](https://liuziwei7.github.io/projects/AAF)
97. Recurrent Iterative Gating Networks for Semantic Segmentation - WACV2019 [<Paper>](https://arxiv.org/abs/1811.08043)
98. Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation - CVPR2018 [<Paper>](http://openaccess.thecvf.com/content_cvpr_2018/papers/Bilinski_Dense_Decoder_Shortcut_CVPR_2018_paper.pdf)
99. DenseASPP for Semantic Segmentation in Street Scenes - CVPR2018 [<Paper>](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf) [<Code-PyTorch>](https://github.com/DeepMotionAIResearch/DenseASPP)
100. Pyramid Attention Network for Semantic Segmentation - 2018 - Face++ [<Paper>](https://arxiv.org/pdf/1805.10180v1.pdf)
101. Autofocus Layer for Semantic Segmentation - 2018 [<Paper](https://arxiv.org/pdf/1805.08403v2.pdf) [<Code-PyTorch>](https://github.com/yaq007/Autofocus-Layer)
102. ExFuse: Enhancing Feature Fusion for Semantic Segmentation - ECCV2018 - Face++ [<Paper>](https://arxiv.org/pdf/1804.03821.pdf)
103. DifNet: Semantic Segmentation by Diffusion Networks - 2018 [<Paper>](https://arxiv.org/pdf/1805.08015v1.pdf)
104. Convolutional CRFs for Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1805.04777v2.pdf)[<Code-PyTorch>](https://github.com/MarvinTeichmann/ConvCRF)
105. ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time - 2018 [<Paper>](https://arxiv.org/pdf/1805.04554v1.pdf)
106. Learning a Discriminative Feature Network for Semantic Segmentation - CVPR2018 - Face++ [<Paper>](https://arxiv.org/abs/1804.09337)
107. Vortex Pooling: Improving Context Representation in Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1804.06242v2.pdf)
108. Fully Convolutional Adaptation Networks for Semantic Segmentation - CVPR2018 [<Paper>](https://arxiv.org/pdf/1804.08286v1.pdf)
109. A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation - 2018 [<Paper>](https://arxiv.org/abs/1804.02032)
110. Context Encoding for Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1803.08904.pdf) [<Code-PyTorch>](https://github.com/zhanghang1989/PyTorch-Encoding) [<Code-PyTorch2>](https://hangzhang.org/PyTorch-Encoding/experiments/segmentation.html) [<Slides>](https://hangzhang.org/slides/EncNet_slides.pdf)
111. ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation - ECCV2018 [<Paper>](https://arxiv.org/pdf/1803.06815v2.pdf) [<Code-Pytorch>](https://github.com/sacmehta/ESPNet)
112. Dynamic-structured Semantic Propagation Network - 2018 - CMU [<Paper>](https://arxiv.org/pdf/1803.06067.pdf)
113. ShuffleSeg: Real-time Semantic Segmentation Network-2018 [<Paper>](https://arxiv.org/pdf/1803.03816v1.pdf) [<Code-TensorFlow>](https://github.com/MSiam/TFSegmentation)
114. RTSeg: Real-time Semantic Segmentation Comparative Study - 2018 [<Paper>](https://arxiv.org/pdf/1803.02758) [<Code-TensorFlow>](https://github.com/MSiam/TFSegmentation)
115. Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1803.02563v1.pdf)
116. DeepLabV3+:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - 2018 - Google [<Paper>](https://arxiv.org/pdf/1802.02611.pdf) [<Code-Tensorflow>](https://github.com/tensorflow/models/tree/master/research/deeplab) [<Code-Karas>](https://github.com/bonlime/keras-deeplab-v3-plus)
117. Adversarial Learning for Semi-Supervised Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1802.07934v1.pdf) [<Code-PyTorch>](https://github.com/hfslyc/AdvSemiSeg)
118. Locally Adaptive Learning Loss for Semantic Image Segmentation - 2018 [<Paper>](https://arxiv.org/pdf/1802.08290v2.pdf)
119. Learning to Adapt Structured Output Space for Semantic Segmentation - 2018 [<Paper>](https://arxiv.org/abs/1802.10349v1.pdf)
120. Improved Image Segmentation via Cost Minimization of Multiple Hypotheses - 2018 [<Paper>](https://arxiv.org/pdf/1802.00088.pdf) [<Code-Matlab>](https://github.com/pubgeo/cmmh_segmentation)
121. TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation - 2018 - Kaggle [<Paper>](https://arxiv.org/abs/1801.05746) [<Code-PyTorch>](https://github.com/ternaus/TernausNet) [<Kaggle-Carvana Image Masking Challenge>](https://www.kaggle.com/c/carvana-image-masking-challenge)
122. Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation - 2018 - Google [<Paper>](https://arxiv.org/pdf/1801.04381.pdf)
123. End-to-end Detection-Segmentation Network With ROI Convolution - 2018 [<Paper>](https://arxiv.org/pdf/1801.02722.pdf)
124. Mix-and-Match Tuning for Self-Supervised Semantic Segmentation - AAAI2018 [<Project>](http://mmlab.ie.cuhk.edu.hk/projects/M&M/) [<Paper>](https://arxiv.org/pdf/1712.00661.pdf) [<Code-Caffe>](https://github.com/XiaohangZhan/mix-and-match)
125. Learning to Segment Every Thing-2017 [<Paper>](https://arxiv.org/pdf/1711.10370.pdf) [<Code-Caffe2>](https://github.com/facebookresearch/Detectron) [<Code-PyTorch>](https://github.com/skrish13/PyTorch-mask-x-rcnn)
126. Deep Dual Learning for Semantic Image Segmentation-2017 [<Paper>](http://personal.ie.cuhk.edu.hk/~pluo/pdf/luoWLWiccv17.pdf)
127. Scene Parsing with Global Context Embedding - ICCV2017 [<Paper>](https://arxiv.org/pdf/1710.06507v1.pdf)
128. FoveaNet: Perspective-aware Urban Scene Parsing - ICCV2017 [<Paper>](http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_FoveaNet_Perspective-Aware_Urban_ICCV_2017_paper.pdf)
129. Segmentation-Aware Convolutional Networks Using Local Attention Masks - 2017 [<Paper>](https://arxiv.org/pdf/1708.04607.pdf) [<Code-Caffe>](https://github.com/aharley/segaware) [<Project>](http://www.cs.cmu.edu/~aharley/segaware/)
130. Stacked Deconvolutional Network for Semantic Segmentation-2017 [<Paper>](https://arxiv.org/pdf/1708.04943.pdf)
131. Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF - CVPR2017 [<Paper>](http://openaccess.thecvf.com/content_cvpr_2017/papers/Shen_Semantic_Segmentation_via_CVPR_2017_paper.pdf) [<Caffe-Code>](https://github.com/FalongShen/SegModel)
132. BlitzNet: A Real-Time Deep Network for Scene Understanding-2017 [<Project>](http://thoth.inrialpes.fr/research/blitznet/) [<Code-Tensorflow>](https://github.com/dvornikita/blitznet) [<Paper>](https://arxiv.org/pdf/1708.02813.pdf)
133. Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation -2017 [<Paper>](https://arxiv.org/pdf/1707.08254v2.pdf) [<Code-Caffe>](https://github.com/SharifAmit/DilatedFCNSegmentation)
134. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation - 2017 [<Paper>](https://arxiv.org/pdf/1707.03718.pdf) [<Code-Torch>](https://github.com/e-lab/LinkNet)
135. Rethinking Atrous Convolution for Semantic Image Segmentation-2017(DeeplabV3) [<Paper>](https://arxiv.org/pdf/1706.05587.pdf)
136. Learning Object Interactions and Descriptions for Semantic Image Segmentation-2017 [<Paper>](http://personal.ie.cuhk.edu.hk/~pluo/pdf/wangLLWcvpr17.pdf)
137. Pixel Deconvolutional Networks-2017 [<Code-Tensorflow>](https://github.com/HongyangGao/PixelDCN) [<Paper>](https://arxiv.org/abs/1705.06820)
138. Dilated Residual Networks-2017 [<Paper>](http://vladlen.info/papers/DRN.pdf) [<Code-PyTorch>](https://github.com/fyu/drn)
139. Recurrent Scene Parsing with Perspective Understanding in the Loop - 2017 [<Project>](http://www.ics.uci.edu/~skong2/recurrentDepthSeg) [<Paper>](https://arxiv.org/abs/1705.07238) [<Code-MatConvNet>](https://github.com/aimerykong/Recurrent-Scene-Parsing-with-Perspective-Understanding-in-the-loop)
140. A Review on Deep Learning Techniques Applied to Semantic Segmentation-2017 [<Paper>](https://arxiv.org/abs/1704.06857)
141. BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks [<Paper>](https://arxiv.org/abs/1706.02135)
142. Efficient ConvNet for Real-time Semantic Segmentation - 2017 [<Paper>](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf)
143. ICNet for Real-Time Semantic Segmentation on High-Resolution Images-2017 [<Project>](https://hszhao.github.io/projects/icnet/) [<Code-Caffe>](https://github.com/hszhao/ICNet) [<Paper>](https://arxiv.org/abs/1704.08545) [<Video>](https://www.youtube.com/watch?v=qWl9idsCuLQ)
144. Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade-2017 [<Paper>](https://arxiv.org/abs/1704.01344) [<Poster>](https://liuziwei7.github.io/papers/layercascade_poster.pdf) [<Project>](https://liuziwei7.github.io/projects/LayerCascade.html) [<Code-Caffe>](https://github.com/liuziwei7/region-conv) [<Slides>](https://liuziwei7.github.io/papers/layercascade_slides.pdf)
145. Loss Max-Pooling for Semantic Image Segmentation-2017 [<Paper>](https://arxiv.org/abs/1704.02966)
146. Annotating Object Instances with a Polygon-RNN-2017 [<Project>](http://www.cs.toronto.edu/polyrnn/) [<Paper>](https://arxiv.org/abs/1704.05548)
147. Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation-2017 [<Project>](https://codeac29.github.io/projects/linknet/) [<Code-Torch7>](https://github.com/e-lab/LinkNet)
148. Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation-2017 [<Paper>](https://arxiv.org/abs/1704.03593)
149. Adversarial Examples for Semantic Image Segmentation-2017 [<Paper>](https://arxiv.org/abs/1703.01101)
150. Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network-2017 [<Paper>](https://arxiv.org/abs/1703.02719)
151. Label Refinement Network for Coarse-to-Fine Semantic Segmentation-2017 [<Paper>](https://www.arxiv.org/abs/1703.00551)
152. **PixelNet: Representation of the pixels, by the pixels, and for the pixels-2017** [<Project>](http://www.cs.cmu.edu/~aayushb/pixelNet/) [<Code-Caffe>](https://github.com/aayushbansal/PixelNet) [<Paper>](https://arxiv.org/abs/1702.06506)
153. LabelBank: Revisiting Global Perspectives for Semantic Segmentation-2017 [<Paper>](https://arxiv.org/abs/1703.09891)
154. Progressively Diffused Networks for Semantic Image Segmentation-2017 [<Paper>](https://arxiv.org/abs/1702.05839)
155. Understanding Convolution for Semantic Segmentation-2017 [<Model-Mxnet>](https://drive.google.com/drive/folders/0B72xLTlRb0SoREhISlhibFZTRmM) [<Mxnet-Code>](https://github.com/TuSimple/TuSimple-DUC/) [<Paper>](https://arxiv.org/abs/1702.08502)
156. Predicting Deeper into the Future of Semantic Segmentation-2017 [<Paper>](https://arxiv.org/abs/1703.07684)
157. **Pyramid Scene Parsing Network-2017** [<Project>](https://hszhao.github.io/projects/pspnet/) [<Code-Caffe>](https://github.com/hszhao/PSPNet) [<Paper>](https://arxiv.org/abs/1612.01105) [<Slides>](http://image-net.org/challenges/talks/2016/SenseCUSceneParsing.pdf)
158. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation-2016 [<Paper>](https://arxiv.org/abs/1612.02649)
159. FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics-2016 [<Code-PyTorch>](https://github.com/GunhoChoi/FusionNet_Pytorch) [<Paper>](https://arxiv.org/abs/1612.05360)
160. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation-2016 [<Code-MatConvNet>](https://github.com/guosheng/refinenet) [<Paper>](https://arxiv.org/abs/1611.06612) [<Code-Pytorch>](https://github.com/thomasjpfan/pytorch_refinenet)
161. Learning from Weak and Noisy Labels for Semantic Segmentation - 2017 [<Paper>](http://repository.kaust.edu.sa/kaust/bitstream/10754/608585/1/07450177.pdf)
162. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation [<Code-Theano>](https://github.com/SimJeg/FC-DenseNet) [<Code-Keras1>](https://github.com/titu1994/Fully-Connected-DenseNets-Semantic-Segmentation) [<Code-Keras2>](https://github.com/0bserver07/One-Hundred-Layers-Tiramisu) [<Paper>](https://arxiv.org/abs/1611.09326)
163. Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes [<Code-Theano>](https://github.com/TobyPDE/FRRN) [<Paper>](https://arxiv.org/abs/1611.08323)
164. PixelNet: Towards a General Pixel-level Architecture-2016 [<Paper>](http://arxiv.org/abs/1609.06694)
165. Recalling Holistic Information for Semantic Segmentation-2016 [<Paper>](https://arxiv.org/abs/1611.08061)
166. Semantic Segmentation using Adversarial Networks-2016 [<Paper>](https://arxiv.org/abs/1611.08408) [<Code-Chainer>](https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks)
167. Region-based semantic segmentation with end-to-end training-2016 [<Paper>](http://arxiv.org/abs/1607.07671)
168. Exploring Context with Deep Structured models for Semantic Segmentation-2016 [<Paper>](https://arxiv.org/abs/1603.03183)
169. Better Image Segmentation by Exploiting Dense Semantic Predictions-2016 [<Paper>](https://arxiv.org/abs/1606.01481)
170. Boundary-aware Instance Segmentation-2016 [<Paper>](https://infoscience.epfl.ch/record/227439/files/HayderHeSalzmannCVPR17.pdf)
171. Improving Fully Convolution Network for Semantic Segmentation-2016 [<Paper>](https://arxiv.org/abs/1611.08986)
172. Deep Structured Features for Semantic Segmentation-2016 [<Paper>](https://arxiv.org/abs/1609.07916)
173. **DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs-2016** [<Project>](http://liangchiehchen.com/projects/DeepLab.html) [<Code-Caffe>](https://bitbucket.org/deeplab/deeplab-public/) [<Code-Tensorflow>](https://github.com/DrSleep/tensorflow-deeplab-resnet) [<Code-PyTorch>](https://github.com/isht7/pytorch-deeplab-resnet) [<Paper>](https://arxiv.org/abs/1606.00915)
174. **DeepLab: Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs-2014** [<Code-Caffe1>](https://bitbucket.org/deeplab/deeplab-public/) [<Code-Caffe2>](https://github.com/TheLegendAli/DeepLab-Context) [<Paper>](http://arxiv.org/abs/1412.7062)
175. Deep Learning Markov Random Field for Semantic Segmentation-2016 [<Project>](https://liuziwei7.github.io/projects/DPN.html) [<Paper>](https://arxiv.org/abs/1606.07230)
176. Convolutional Random Walk Networks for Semantic Image Segmentation-2016 [<Paper>](https://arxiv.org/abs/1605.07681)
177. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016 [<Code-Caffe1>](https://github.com/e-lab/ENet-training) [<Code-Caffe2>](https://github.com/TimoSaemann/ENet) [<Paper>](https://arxiv.org/abs/1606.02147) [<Blog>](https://culurciello.github.io/tech/2016/06/20/training-enet.html)
178. High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks-2016 [<Paper>](https://arxiv.org/abs/1604.04339)
179. ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation-2016 [<Paper>](http://arxiv.org/abs/1604.05144)
180. Object Boundary Guided Semantic Segmentation-2016 [<Code-Caffe>](https://github.com/twtygqyy/obg_fcn) [<Paper>](http://arxiv.org/abs/1603.09742)
181. Segmentation from Natural Language Expressions-2016 [<Project>](http://ronghanghu.com/text_objseg/) [<Code-Tensorflow>](https://github.com/ronghanghu/text_objseg) [<Code-Caffe>](https://github.com/Seth-Park/text_objseg_caffe) [<Paper>](http://arxiv.org/abs/1603.06180)
182. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation-2016 [<Code-Caffe>](https://github.com/kolesman/SEC) [<Paper>](https://arxiv.org/abs/1603.06098)
183. Global Deconvolutional Networks for Semantic Segmentation-2016 [<Paper>](https://arxiv.org/abs/1602.03930) [<Code-Caffe>](https://github.com/DrSleep/GDN)
184. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network-2015 [<Project>](http://cvlab.postech.ac.kr/research/transfernet/) [<Code-Caffe>](https://github.com/maga33/TransferNet) [<Paper>](http://arxiv.org/abs/1512.07928)
185. Learning Dense Convolutional Embeddings for Semantic Segmentation-2015 [<Paper>](https://arxiv.org/abs/1511.04377)
186. ParseNet: Looking Wider to See Better-2015 [<Code-Caffe>](https://github.com/weiliu89/caffe/tree/fcn) [<Model-Caffe>](https://github.com/BVLC/caffe/wiki/Model-Zoo#parsenet-looking-wider-to-see-better) [<Paper>](http://arxiv.org/abs/1506.04579)
187. Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation-2015 [<Project>](http://cvlab.postech.ac.kr/research/decouplednet/) [<Code-Caffe>](https://github.com/HyeonwooNoh/DecoupledNet) [<Paper>](http://arxiv.org/abs/1506.04924)
188. **SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation-2015** [<Project>](http://mi.eng.cam.ac.uk/projects/segnet/) [<Code-Caffe>](https://github.com/alexgkendall/caffe-segnet) [<Paper>](http://arxiv.org/abs/1511.00561) [<Tutorial1>](http://mi.eng.cam.ac.uk/projects/segnet/tutorial.html) [<Tutorial2>](https://github.com/alexgkendall/SegNet-Tutorial)
189. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling-2015 [<Code-Caffe>](https://github.com/alexgkendall/caffe-segnet) [<Code-Chainer>](https://github.com/pfnet-research/chainer-segnet) [<Paper>](http://arxiv.org/abs/1505.07293)
190. Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform-2015 [<Paper>](https://arxiv.org/abs/1511.03328)
191. Semantic Segmentation with Boundary Neural Fields-2015 [<Code-Matlab>](https://github.com/gberta/BNF_globalization) [<Paper>](https://arxiv.org/abs/1511.02674)
192. Semantic Image Segmentation via Deep Parsing Network-2015 [<Project>](http://personal.ie.cuhk.edu.hk/~lz013/projects/DPN.html) [<Paper1>](http://arxiv.org/abs/1509.02634) [<Paper2>](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Liu_Semantic_Image_Segmentation_ICCV_2015_paper.pdf) [<Slides>](http://personal.ie.cuhk.edu.hk/~pluo/pdf/presentation_dpn.pdf)
193. What’s the Point: Semantic Segmentation with Point Supervision-2015  [<Project>](http://vision.stanford.edu/whats_the_point/) [<Code-Caffe>](https://github.com/abearman/whats-the-point1) [<Model-Caffe>](http://vision.stanford.edu/whats_the_point/models.html) [<Paper>](https://arxiv.org/abs/1506.02106)
194. U-Net: Convolutional Networks for Biomedical Image Segmentation-2015  [<Project>](http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/) [<Code+Data>](http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz) [<Code-Keras>](https://github.com/orobix/retina-unet) [<Code-Tensorflow>](https://github.com/jakeret/tf_unet) [<Paper>](http://arxiv.org/abs/1505.04597) [<Notes>](http://zongwei.leanote.com/post/Pa)
195. Learning Deconvolution Network for Semantic Segmentation(DeconvNet)-2015  [<Project>](http://cvlab.postech.ac.kr/research/deconvnet/) [<Code-Caffe>](https://github.com/HyeonwooNoh/DeconvNet) [<Paper>](http://arxiv.org/abs/1505.04366) [<Slides>](http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w06-deconvnet.pdf)
196. Multi-scale Context Aggregation by Dilated Convolutions-2015  [<Project>](http://vladlen.info/publications/multi-scale-context-aggregation-by-dilated-convolutions/) [<Code-Caffe>](https://github.com/fyu/dilation) [<Code-Keras>](https://github.com/nicolov/segmentation_keras) [<Paper>](http://arxiv.org/abs/1511.07122) [<Notes>](http://www.inference.vc/dilated-convolutions-and-kronecker-factorisation/)
197. ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation-2015  [<Code-Theano>](https://github.com/fvisin/reseg) [<Paper>](https://arxiv.org/abs/1511.07053)
198. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation-2015  [<Paper>](https://arxiv.org/abs/1503.01640)
199. Feedforward semantic segmentation with zoom-out features-2015  [<Code-Torch>](https://bitbucket.org/m_mostajabi/zoom-out-release) [<Paper>](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf) [<Video>](https://www.youtube.com/watch?v=HvgvX1LXQa8)
200. Conditional Random Fields as Recurrent Neural Networks-2015  [<Project>](http://www.robots.ox.ac.uk/~szheng/CRFasRNN.html) [<Code-Caffe1>](https://github.com/torrvision/crfasrnn) [<Code-Caffe2>](https://github.com/martinkersner/train-CRF-RNN) [<Demo>](http://www.robots.ox.ac.uk/~szheng/crfasrnndemo) [<Paper1>](http://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf) [<Paper2>](http://arxiv.org/abs/1502.03240)
201. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation-2015 [<Paper>](https://arxiv.org/abs/1504.01013)
202. **Fully Convolutional Networks for Semantic Segmentation-2015**  [<Code-Caffe>](https://github.com/shelhamer/fcn.berkeleyvision.org) [<Model-Caffe>](https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn) [<Code-Tensorflow1>](https://github.com/MarvinTeichmann/tensorflow-fcn) [<Code-Tensorflow2>](https://github.com/shekkizh/FCN.tensorflow) [<Code-Chainer>](https://github.com/wkentaro/fcn) [<Code-PyTorch>](https://github.com/wkentaro/pytorch-fcn) [<Paper1>](http://arxiv.org/abs/1411.4038) [<Paper2>](http://arxiv.org/abs/1605.06211) [<Slides1>](https://docs.google.com/presentation/d/1VeWFMpZ8XN7OC3URZP4WdXvOGYckoFWGVN7hApoXVnc) [<Slides2>](http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-pixels.pdf)
203. Deep Joint Task Learning for Generic Object Extraction-2014  [<Project>](http://vision.sysu.edu.cn/projects/deep-joint-task-learning/) [<Code-Caffe>](https://github.com/xiaolonw/nips14_loc_seg_testonly) [<Dataset>](http://objectextraction.github.io/) [<Paper>](http://ss.sysu.edu.cn/~ll/files/NIPS2014_JointTask.pdf)
204. Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification-2014 [<Code-Caffe>](https://dl.dropboxusercontent.com/u/6448899/caffe.zip) [<Paper>](https://arxiv.org/abs/1412.4526)

## 2. Panoptic Segmentation

1. Real-Time Panoptic Segmentation from Dense Detections - 2019 [<Paper>](https://arxiv.org/pdf/1912.01202.pdf)
2. UPSNet: A Unified Panoptic Segmentation Network - CVPR2019 [<Paper>](https://arxiv.org/pdf/1901.03784.pdf)
3. An End-to-end Network for Panoptic Segmentation - Face++ - CVPR2019 [<Paper>]() 
4. Attention-guided Unified Network for Panoptic Segmentation - CVPR2019 [<Paper>](https://arxiv.org/pdf/1812.03904.pdf)
5. Single Network Panoptic Segmentation for Street Scene Understanding - 2019 [<Paper>](https://arxiv.org/pdf/1902.02678.pdf)
6. Panoptic Feature Pyramid Networks - CVPR2019 [<Paper>](https://arxiv.org/pdf/1901.02446.pdf)
7. DeeperLab: Single-Shot Image Parser - 2019 [<Paper>](https://arxiv.org/pdf/1902.05093.pdf)
8. Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network - 2019 [<Paper>](https://arxiv.org/pdf/1809.02110v1.pdf)
9. Weakly- and Semi-Supervised Panoptic Segmentation - ECCV2018 [<Paper>](https://arxiv.org/abs/1808.03575) [<Code-Matlab>](https://github.com/qizhuli/Weakly-Supervised-Panoptic-Segmentation) [<Project>](https://qizhuli.github.io/publication/weakly-supervised-panoptic-segmentation/) [<Poster>](http://www.robots.ox.ac.uk/~liqizhu/projects/eccv_2018/Poster_ECCV_2018.pdf)
10. Panoptic Segmentation - FAIR2018(CVPR2019)  [<Paper>](https://arxiv.org/pdf/1801.00868.pdf) [<Paper-CVPR2019>](http://openaccess.thecvf.com/content_CVPR_2019/papers/Kirillov_Panoptic_Segmentation_CVPR_2019_paper.pdf)

## 3. Human Parsing

1. Graphonomy: Universal Human Parsing via Graph Transfer Learning - CVPR2019 [<Paper>](ttps://arxiv.org/abs/1904.04536) [<Code-PyTorch>](https://github.com/Gaoyiminggithub/Graphonomy)
2. Macro-Micro Adversarial Network for Human Parsing - ECCV2018 [<Paper>](https://arxiv.org/abs/1807.08260) [<Code-PyTorch>](https://github.com/RoyalVane/MMAN)
3. Holistic, Instance-level Human Parsing - 2017  [<Paper>](https://arxiv.org/pdf/1709.03612.pdf)
4. Semi-Supervised Hierarchical Semantic Object Parsing - 2017  [<Paper>](https://arxiv.org/pdf/1709.08019.pdf)
5. Towards Real World Human Parsing: Multiple-Human Parsing in the Wild - 2017  [<Paper>](https://arxiv.org/pdf/1705.07206.pdf)
6. Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing-2017  [<Project>](http://hcp.sysu.edu.cn/lip/) [<Code-Caffe>](https://github.com/Engineering-Course/LIP_SSL) [<Paper>](https://arxiv.org/abs/1703.05446)
7. Efficient and Robust Deep Networks for Semantic Segmentation - 2017  [<Paper>](https://lmb.informatik.uni-freiburg.de/Publications/2017/OB17a/oliveira17ijrr.pdf) [<Project>](https://lmb.informatik.uni-freiburg.de/Publications/2017/OB17a/) [<Code-Caffe>](https://lmb.informatik.uni-freiburg.de/resources/binaries/PartSeg/caffe_FAST.tar.gz)
8. **Deep Learning for Human Part Discovery in Images-2016**  [<Code-Chainer>](https://github.com/shiba24/deep-learning-for-human-part-discovery-in-images) [<Paper>](http://lmb.informatik.uni-freiburg.de/Publications/2016/OB16a/oliveira16icra.pdf)
9. **A CNN Cascade for Landmark Guided Semantic Part Segmentation-2016**  [<Project>](http://aaronsplace.co.uk/papers/jackson2016guided/) [<Paper>](https://arxiv.org/abs/1609.09642)
10. Deep Learning for Semantic Part Segmentation With High-level Guidance-2015  [<Paper>](https://arxiv.org/abs/1505.02438)
11. Neural Activation Constellations-Unsupervised Part Model Discovery with Convolutional Networks-2015  [<Paper>](https://arxiv.org/abs/1504.08289)
12. Human Parsing with Contextualized Convolutional Neural Network-2015  [<Paper>](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Liang_Human_Parsing_With_ICCV_2015_paper.pdf)
13. Part detector discovery in deep convolutional neural networks-2014  [<Code-Matlab>](https://github.com/cvjena/PartDetectorDisovery) [<Paper>](https://arxiv.org/abs/1411.3159)

## 4. Clothes Parsing

1. Looking at Outfit to Parse Clothing-2017  [<Paper>](https://arxiv.org/abs/1703.01386)

2. Semantic Object Parsing with Local-Global Long Short-Term Memory-2015  [<Paper>](https://arxiv.org/abs/1511.04510)

3. A High Performance CRF Model for Clothes Parsing-2014  [<Project>](http://hi.cs.waseda.ac.jp/~esimo/en/code/clothes_parsing/) [<Code-Matlab>](https://github.com/bobbens/clothes_parsing) [<Dataset>](http://vision.is.tohoku.ac.jp/~kyamagu/ja/research/clothing_parsing/) [<Paper>](https://pdfs.semanticscholar.org/0416/f5d1564d1f2a597acac04e81b02b2eff67d2.pdf)

4. Clothing co-parsing by joint image segmentation and labeling-2013 [<Project>](http://hcp.sysu.edu.cn/clothing-co-parsing-by-joint-image-segmentation-and-labeling/) [<Dataset>](https://github.com/bearpaw/clothing-co-parsing) [<Paper>](https://arxiv.org/abs/1502.00739)

5. Parsing clothing in fashion photographs-2012  [<Project>](http://vision.is.tohoku.ac.jp/~kyamagu/research/clothing_parsing/) [<Paper>](http://vision.is.tohoku.ac.jp/~kyamagu/papers/yamaguchi_cvpr2012.pdf)

## 5. Instance Segmentation

1. Deep Snake for Real-Time Instance Segmentation - CVPR2020 [<Paper>](https://arxiv.org/pdf/2001.01629.pdf) [<Code-PyTorch>](https://github.com/zju3dv/snake)
2. SOLO: Segmenting Objects by Locations - 2019 - ByteDance [<Paper>](https://arxiv.org/pdf/1912.04488.pdf)
3. YOLACT: Real-time Instance Segmentation - 2019 [<Paper>](https://arxiv.org/abs/1904.02689) [<Code-PyTorch>](https://github.com/dbolya/yolact)
4. Pose2Seg: Detection Free Human Instance Segmentation - CVPR2019 [<Paper>](https://arxiv.org/abs/1803.10683) [<Code-PyTorch>]( https://github.com/liruilong940607/Pose2Seg) [<Project>](http://www.liruilong.cn/Pose2Seg/index.html) [<Dataset>](https://cg.cs.tsinghua.edu.cn/dataset/form.html?dataset=ochuman)
5. Mask Scoring R-CNN - CVPR2019 [<Paper>](https://arxiv.org/abs/1903.00241) [<Code-PyTorch>](https://github.com/zjhuang22/maskscoring_rcnn)
6. Actor-Critic Instance Segmentation - CVPR2019 [<Paper>](https://arxiv.org/abs/1904.05126)
7. TensorMask: A Foundation for Dense Object Segmentation - FAIR [<Paper>](https://arxiv.org/abs/1903.12174)
8. A Pyramid CNN for Dense-Leaves Segmentation - 2018 [<Paper>](https://arxiv.org/abs/1804.01646)
9. Predicting Future Instance Segmentations by Forecasting Convolutional Features - 2018 [<Paper>](https://arxiv.org/abs/1803.11496)
10. Path Aggregation Network for Instance Segmentation - CVPR2018  [<Paper>](https://arxiv.org/pdf/1803.01534.pdf) [<Code-PyTorch>](https://github.com/ShuLiu1993/PANet)
11. PixelLink: Detecting Scene Text via Instance Segmentation - AAAI2018  [<Code-Tensorflow>](https://github.com/ZJULearning/pixel_link) [<Paper>](https://arxiv.org/pdf/1801.01315.pdf)
12. MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features - 2017 - google [<Paper>](https://arxiv.org/pdf/1712.04837v1.pdf)
13. Recurrent Neural Networks for Semantic Instance Segmentation-2017  [<Paper>](https://arxiv.org/pdf/1712.00617v1.pdf)
14. Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017  [<Paper>](https://arxiv.org/abs/1704.02386)
15. Semantic Instance Segmentation via Deep Metric Learning-2017  [<Paper>](https://arxiv.org/abs/1703.10277)
16. Mask R-CNN-2017  [<Code-Tensorflow>](https://github.com/CharlesShang/FastMaskRCNN) [<Paper>](https://arxiv.org/abs/1703.06870) [<Code-Caffe2>](https://github.com/facebookresearch/Detectron) [<Code-Karas>](https://github.com/matterport/Mask_RCNN/) [<Code-PyTorch>](https://github.com/soeaver/Pytorch_Mask_RCNN/) [<Code-MXNet>](https://github.com/TuSimple/mx-maskrcnn)
17. Pose2Seg: Human Instance Segmentation Without Detection - 2018 [<Paper>](https://arxiv.org/abs/1803.10683)
18. Pose2Instance: Harnessing Keypoints for Person Instance Segmentation-2017 [<Paper>](https://arxiv.org/abs/1704.01152)
19. Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 [<Paper>](https://arxiv.org/abs/1704.02386)
20. Semantic Instance Segmentation with a Discriminative Loss Function-2017  [<Paper>](https://arxiv.org/pdf/1708.02551v1.pdf)
21. Fully Convolutional Instance-aware Semantic Segmentation-2016  [<Code-MXNet>](https://github.com/msracver/FCIS) [<Paper>](https://arxiv.org/abs/1611.07709)
22. End-to-End Instance Segmentation with Recurrent Attention  [<Paper>](https://arxiv.org/abs/1605.09410v3) [<Code-Tensorflow>](https://github.com/renmengye/rec-attend-public)
23. **Instance-aware Semantic Segmentation via Multi-task Network Cascades-2015**  [<Code-Caffe>](https://github.com/daijifeng001/MNC) [<Paper>](http://arxiv.org/abs/1512.04412)
24. Recurrent Instance Segmentation-2015  [<Project>](http://romera-paredes.com/ris) [<Code-Torch7>](https://github.com/bernard24/ris) [<Paper>](http://arxiv.org/abs/1511.08250) [<Poster>](http://www.eccv2016.org/files/posters/P-4B-46.pdf) [<Video>](https://www.youtube.com/watch?v=l_WD2OWOqBk)

## 6. Segment Object Candidates

1. Contextual Encoder-Decoder Network for Visual Saliency Prediction - 2019 [<Paper>](https://arxiv.org/abs/1902.06634)
2. FastMask: Segment Object Multi-scale Candidates in One Shot-2016  [<Code-Caffe>](https://github.com/voidrank/FastMask) [<Paper>](https://arxiv.org/abs/1612.08843)
3. Learning to Refine Object Segments-2016  [<Code-Torch>](https://github.com/facebookresearch/deepmask) [<Paper>](http://arxiv.org/abs/1603.08695)
4. Learning to Segment Object Candidates-2015  [<Code-Torch>](https://github.com/facebookresearch/deepmask) [<Code-Theano-Keras>](https://github.com/abbypa/NNProject_DeepMask) [<Paper>](http://arxiv.org/abs/1506.06204)

## 7. Foreground Object Segmentation

1. **Pixel Objectness-2017** [<Project>](http://vision.cs.utexas.edu/projects/pixelobjectness/) [<Code-Caffe>](https://github.com/suyogduttjain/pixelobjectness) [<Paper>](https://arxiv.org/abs/1701.05349)

2. A Deep Convolutional Neural Network for Background Subtraction-2017  [<Paper>](https://arxiv.org/abs/1702.01731)

1. Semantic Segmentation

FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale
Context Aggregation and Feature Space Super-resolution - 2020 - SenseTime <Paper>
RGPNet: A Real-Time General Purpose Semantic Segmentation - 2019 <Paper>
Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation - 2019 <Paper>
Document Structure Extraction for Forms using Very High Resolution Semantic Segmentation - 2019 - Adobe 文档结构提取 <Paper>
Class-Conditional Domain Adaptation on Semantic Segmentation - 2019 <Paper>
On Symbiosis of Attribute Prediction and Semantic Segmentation - 2019 <Paper>
Differentiable Meta-learning Model for Few-shot Semantic Segmentation - 2019 <Paper>
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach - 2019 <Paper>
Real-Time Semantic Segmentation via Multiply Spatial Fusion Network - 2019 旷视 <Paper>
Improving Semantic Segmentation of Aerial Images Using Patch-based Attention - 2019 <Paper>
Location-aware Upsampling for Semantic Segmentation - 2019 - CAS <Paper> <Code-PyTorch>
Knowledge Distillation for Incremental Learning in Semantic Segmentation - 2019 <Paper>
Eye Semantic Segmentation with A Lightweight Model - 2019 眼部分割 <Paper>
Distilling Pixel-Wise Feature Similarities for Semantic Segmentation - 2019 <Paper>
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation - 2019 道路分割 <Paper>
Multi-source Domain Adaptation for Semantic Segmentation - 2019 - NeurIPS <Paper> <Code-Tensorflow>
Region Mutual Information Loss for Semantic Segmentation - 2019 - NeurIPS <Paper> <Code-PyTorch>
Correlation Maximized Structural Similarity Loss for Semantic Segmentation - 2019 <Paper>
Deep Semantic Segmentation of Natural and Medical Images: A Review - 2019 医学图像分割综述 <Paper>
CNN-based Semantic Segmentation using Level Set Loss - 2019 KAIST <Paper>
Domain Adaptation for Semantic Segmentation with Maximum Squares Loss - 2019 <Paper> <Code-PyTorch>
Distributed Iterative Gating Networks for Semantic Segmentation - 2019 <Paper>
Adaptive Class Weight based Dual Focal Loss for Improved Semantic Segmentation - 2019 <Paper>
Object-Contextual Representations for Semantic Segmentation - 2019 <Paper>
ACFNet: Attentional Class Feature Network for Semantic Segmentation - 2019 <Paper>
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation -2019 <Paper>
Feature Pyramid Encoding Network for Real-time Semantic Segmentation - 2019 <Paper>
Graph-guided Architecture Search for Real-time Semantic Segmentation - 2019 <Paper>
Dual Graph Convolutional Network for Semantic Segmentation - 2019 <Paper>
Squeeze-and-Attention Networks for Semantic Segmentation - 2019 <Paper>
Semantic Segmentation of Panoramic Images Using a Synthetic Dataset - 2019 <Paper> <Code-Github>
See More Than Once – Kernel-Sharing Atrous Convolution for Semantic Segmentation - 2019 <Paper>
Feedbackward Decoding for Semantic Segmentation - 2019 <Paper>
Asymmetric Non-local Neural Networks for Semantic Segmentation - 2019 - ICCV <Paper> <Code-PyTorch>
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment - 2019 <Paper>
Semi-Supervised Semantic Segmentation with High- and Low-level Consistency - 2019 <Paper>
Benchmarking the Robustness of Semantic Segmentation Models - 2019 <Paper> <Homepage>
Distance Map Loss Penalty Term for Semantic Segmentation - 2019 <Paper>
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation - 2019 <Paper>
SqueezeNAS: Fast neural architecture search for faster semantic segmentation - 2019 - DeepScale <Paper>
Expectation-Maximization Attention Networks for Semantic Segmentation - 2019 <Paper>
Incremental Learning Techniques for Semantic Segmentation - 2019 <Paper>
Interlaced Sparse Self-Attention for Semantic Segmentation - 2019 <Paper>
DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation - 2019 <Paper>
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation - 2019 <Paper> <Code-PyTorch>
Cross Attention Network for Semantic Segmentation - 2019 <Paper>
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks - 2019 <Paper>
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries - 2019 <Paper>
Improving Semantic Segmentation via Dilated Affinity - 2019 <Paper>
Adaptive Context Encoding Module for Semantic Segmentation - 2019 <Paper>
A Regularized Convolutional Neural Network for Semantic Image Segmentation - 2019 <Paper>
Deep Learning-Based Semantic Segmentation of Microscale Objects - 2019 生物细胞分割 <Paper>
SAN: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images - 2019 高分辨率卫星图像分割 <Paper>
Hard Pixels Mining: Learning Using Privileged Information for Semantic Segmentation - 2019 <Paper>
ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation - 2019 <Paper>
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things - 2019 <Paper>
Universal Barcode Detector via Semantic Segmentation - 2019 条形码分割 <Paper>
Cross-view Semantic Segmentation for Sensing Surroundings - 2019 <Paper> <Code-PyTorch> <Project>
Zero-Shot Semantic Segmentation - 2019 - NeurlPS <Paper> <Code-Github>
Implicit Background Estimation for Semantic Segmentation - 2019 <Paper> <Code-PyTorch>
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation - 2019 - NVIDIA <Paper> <Project>
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation - 2019 <Paper> <Project> <Code-PyTorch>
Structured Knowledge Distillation for Semantic Segmentation - CVPR2019 <Paper>
Co-Occurrent Features in Semantic Segmentation - CVPR2019 <Paper>
Semantic Projection Network for Zero- and Few-Label Semantic Segmentation - CVPR2019 <Paper>
Context-Reinforced Semantic Segmentation - CVPR2019 <Paper>
SwiftNet - In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images - CVPR2019 <Paper> <Code-PyTorch>
All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation - CVPR2019 <Paper>
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection - CVPR2019 <Paper>
Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach - CVPR2019 <Paper>
Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation - CVPR2019 <Paper>
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation - CVPR2019 <Paper>
Geometry-Aware Distillation for Indoor Semantic Segmentation - CVPR2019 <Paper>
Seamless Scene Segmentation - CVPR2019 <Paper>
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation - CVPR2019 <Paper> <Code-Github>
Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation - CVPR2019 <Paper>
PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding - CVPR2019 <Paper> <Homepage>
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation - CVPR2019 <Paper>
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation-Megvii-2019 <Paper>
DADA: Depth-aware Domain Adaptation in Semantic Segmentation - 2019 <Paper>
GFF: Gated Fully Fusion for Semantic Segmentation - 2019 <Paper>
DSNet: An Efficient CNN for Road Scene Segmentation - 2019 <Paper>
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference - CVPR2019 <Paper>
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions - 2019 <Paper>
Fast-SCNN: Fast Semantic Segmentation Network - 2019 <Paper>
Data augmentation using learned transforms for one-shot medical image segmentation - CVPR2019 <Paper>
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation - 2019 <Paper>
CCNet: Criss-Cross Attention for Semantic Segmentation - 2018 <Paper> <Code-PyTorch>
A PyTorch Semantic Segmentation Toolbox - 2018 <Paper> <Code-PyTorch>
ShelfNet for Real-time Semantic Segmentation - 2018 <Paper> <Code-PyTorch>
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training - ECCV2018 <Paper> <Project> <Code-MXNet>
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction - 2018 - Deeplab <Paper> <Code-Deeplab-Tensorflow>
Light-Weight RefineNet for Real-Time Semantic Segmentation - bmvc2018 <Paper> <Code-Torch>
Dual Attention Network for Scene Segmentation - 2018 <Paper>
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation - ECCV 2018 - Face++ <Paper> <Code-PyTorch>
Adaptive Affinity Field for Semantic Segmentation - ECCV2018 <Paper> <HomePage>
Recurrent Iterative Gating Networks for Semantic Segmentation - WACV2019 <Paper>
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation - CVPR2018 <Paper>
DenseASPP for Semantic Segmentation in Street Scenes - CVPR2018 <Paper> <Code-PyTorch>
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 - ECCV2018 - 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> <Code-PyTorch2> <Slides>
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation - ECCV2018 <Paper> <Code-Pytorch>
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 - ICCV2017 <Paper>
FoveaNet: Perspective-aware Urban Scene Parsing - ICCV2017 <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-Caffe> <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> <Code-Pytorch>
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-Matlab> <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-Torch> <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>

2. Panoptic Segmentation

Real-Time Panoptic Segmentation from Dense Detections - 2019 <Paper>
UPSNet: A Unified Panoptic Segmentation Network - CVPR2019 <Paper>
An End-to-end Network for Panoptic Segmentation - Face++ - CVPR2019 [<Paper>]()
Attention-guided Unified Network for Panoptic Segmentation - CVPR2019 <Paper>
Single Network Panoptic Segmentation for Street Scene Understanding - 2019 <Paper>
Panoptic Feature Pyramid Networks - CVPR2019 <Paper>
DeeperLab: Single-Shot Image Parser - 2019 <Paper>
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network - 2019 <Paper>
Weakly- and Semi-Supervised Panoptic Segmentation - ECCV2018 <Paper> <Code-Matlab> <Project> <Poster>
Panoptic Segmentation - FAIR2018(CVPR2019) <Paper> <Paper-CVPR2019>

3. Human Parsing

Graphonomy: Universal Human Parsing via Graph Transfer Learning - CVPR2019 <Paper> <Code-PyTorch>
Macro-Micro Adversarial Network for Human Parsing - ECCV2018 <Paper> <Code-PyTorch>
Holistic, Instance-level Human Parsing - 2017 <Paper>
Semi-Supervised Hierarchical Semantic Object Parsing - 2017 <Paper>
Towards Real World Human Parsing: Multiple-Human Parsing in the Wild - 2017 <Paper>
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing-2017 <Project> <Code-Caffe> <Paper>
Efficient and Robust Deep Networks for Semantic Segmentation - 2017 <Paper> <Project> <Code-Caffe>
Deep Learning for Human Part Discovery in Images-2016 <Code-Chainer> <Paper>
A CNN Cascade for Landmark Guided Semantic Part Segmentation-2016 <Project> <Paper>
Deep Learning for Semantic Part Segmentation With High-level Guidance-2015 <Paper>
Neural Activation Constellations-Unsupervised Part Model Discovery with Convolutional Networks-2015 <Paper>
Human Parsing with Contextualized Convolutional Neural Network-2015 <Paper>
Part detector discovery in deep convolutional neural networks-2014 <Code-Matlab> <Paper>

4. Clothes Parsing

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

5. Instance Segmentation

Deep Snake for Real-Time Instance Segmentation - CVPR2020 <Paper> <Code-PyTorch>
SOLO: Segmenting Objects by Locations - 2019 - ByteDance <Paper>
YOLACT: Real-time Instance Segmentation - 2019 <Paper> <Code-PyTorch>
Pose2Seg: Detection Free Human Instance Segmentation - CVPR2019 <Paper> <Code-PyTorch> <Project> <Dataset>
Mask Scoring R-CNN - CVPR2019 <Paper> <Code-PyTorch>
Actor-Critic Instance Segmentation - CVPR2019 <Paper>
TensorMask: A Foundation for Dense Object Segmentation - FAIR <Paper>
A Pyramid CNN for Dense-Leaves Segmentation - 2018 <Paper>
Predicting Future Instance Segmentations by Forecasting Convolutional Features - 2018 <Paper>
Path Aggregation Network for Instance Segmentation - CVPR2018 <Paper> <Code-PyTorch>
PixelLink: Detecting Scene Text via Instance Segmentation - AAAI2018 <Code-Tensorflow> <Paper>
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features - 2017 - google <Paper>
Recurrent Neural Networks for Semantic Instance Segmentation-2017 <Paper>
Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 <Paper>
Semantic Instance Segmentation via Deep Metric Learning-2017 <Paper>
Mask R-CNN-2017 <Code-Tensorflow> <Paper> <Code-Caffe2> <Code-Karas> <Code-PyTorch> <Code-MXNet>
Pose2Seg: Human Instance Segmentation Without Detection - 2018 <Paper>
Pose2Instance: Harnessing Keypoints for Person Instance Segmentation-2017 <Paper>
Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 <Paper>
Semantic Instance Segmentation with a Discriminative Loss Function-2017 <Paper>
Fully Convolutional Instance-aware Semantic Segmentation-2016 <Code-MXNet> <Paper>
End-to-End Instance Segmentation with Recurrent Attention <Paper> <Code-Tensorflow>
Instance-aware Semantic Segmentation via Multi-task Network Cascades-2015 <Code-Caffe> <Paper>
Recurrent Instance Segmentation-2015 <Project> <Code-Torch7> <Paper> <Poster> <Video>

6. Segment Object Candidates

Contextual Encoder-Decoder Network for Visual Saliency Prediction - 2019 <Paper>
FastMask: Segment Object Multi-scale Candidates in One Shot-2016 <Code-Caffe> <Paper>
Learning to Refine Object Segments-2016 <Code-Torch> <Paper>
Learning to Segment Object Candidates-2015 <Code-Torch> <Code-Theano-Keras> <Paper>

7. Foreground Object Segmentation

Pixel Objectness-2017 <Project> <Code-Caffe> <Paper>
A Deep Convolutional Neural Network for Background Subtraction-2017 <Paper>

Last modification：March 10, 2020

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41 comments

叶玉伟
February 1st, 2021 at 05:47 pm

博主你好，想问一下有关于dense net实现图像分割的吗

Reply
jihong
April 2nd, 2020 at 10:30 am

请问博主有下载好的原文可以发个链接吗？

Reply
1. AIHGF
  April 6th, 2020 at 11:05 am
  
  @jihong
  
  如果是链接是 arxiv.org 国内访问速度慢，可以访问 http://cn.arxiv.org，输入论文的id，即可.
  
  Reply
2. jihong
  April 2nd, 2020 at 10:39 am
  
  @jihong
  
  有些想看的论文下载不到
  
  Reply
yearing1017
December 5th, 2019 at 05:25 pm

博主您好，我是一个研一的学生，正在入门语义分割。目前有个问题想请教博主，我在分割一些无人机矿区影像的时候，有一类是“车辙”，也就是车轮的痕迹，在分割的时候，该像素点的识别正确率很低。我想借助一些抠图的算法，因为有的抠图算法精细到了“头发丝”，想将两者结合起来，请问博主您有推荐的论文和方向思路吗？

Reply
1. AIHGF
  December 5th, 2019 at 09:55 pm
  
  @yearing1017
  
  抠图的像素级可能和你想的像素级不是一个概念. 你针对的任务"车辙"分割，实际上是一种像素级分类，对影响图像的每个像素进行是否车辙的类别分割.抠图或者说软分割，是类别无关的. 或者说是具体场景的抠图，如阿里的人物抠图，也是基于分割，抠图只是起到改善边缘等细节的效果.
  
  Reply
咸鱼
October 22nd, 2019 at 10:03 am

博主有没有关注过图卷积网络结合语义分割的？

Reply
1. AIHGF
  October 22nd, 2019 at 10:56 am
  
  @咸鱼
  
  近期没太关注图卷积网络
  
  Reply
yy9199
September 20th, 2019 at 08:32 pm

请问博主这里面的代码有复现的吗？有没有推荐？比如说哪个报错比较好解决的，哪个环境满足了基本不报错的

Reply
1. AIHGF
  September 20th, 2019 at 09:03 pm
  
  @yy9199
  
  很多都容易复现的，比如 FCN、DeepLab、UNet、Mask R-CNN 等经典的
  
  Reply
  1. yy9199
    October 18th, 2019 at 10:17 am
    
    @AIHGF
    
    好的，谢谢
    
    Reply
jay
July 25th, 2019 at 03:11 pm

请问有目标检测相关论文阅读的栏目吗

Reply
卡卡
May 7th, 2019 at 04:54 pm

一些关于语义分割的代码：
code-pytorch：https://github.com/Tramac/awesome-semantic-segmentation-pytorch
code-tensorflow：https://github.com/GeorgeSeif/Semantic-Segmentation-Suite

Reply
1. 搬仓~
  November 14th, 2019 at 12:48 pm
  
  @卡卡
  
  第二个网站，程序里边下载模型，是在网址上下载的，然后一直报错Command '['python', 'Seg_utils/get_pretrained_checkpoints.py', '--model=ResNet101']' returned non-zero exit status 1.
  
  Reply
  1. 尕娃
    November 26th, 2019 at 01:34 pm
    
    @搬仓~
    
    你好，请问这个问题你解决了么
    
    Reply
2. 搬仓~
  November 14th, 2019 at 12:46 pm
  
  @卡卡
  
  我找了好久，前几天进了这个网站，但是运行的时候出了问题，可以qq联系一下吗？714005018求大神指导！
  
  Reply
3. yy9199
  July 9th, 2019 at 11:22 am
  
  @卡卡
  
  请问你这些代码合集运行的时候需要把全部数据集下载好吗？会不会每个net需要的库不一样，运行不同的代码需要下载不同的库？这些NET有运行顺序吗？
  
  Reply
  1. AIHGF
    July 9th, 2019 at 12:31 pm
    
    @yy9199
    
    可以了解下 python 使用不同的库，如虚拟环境
    
    Reply
4. 卡卡
  May 7th, 2019 at 06:32 pm
  
  @卡卡
  
  Fast-SCNN-code: https://github.com/Tramac/Fast-SCNN-pytorch
  
  Reply
5. AIHGF
  May 7th, 2019 at 05:19 pm
  
  @卡卡
  
  感谢！
  
  Reply
  1. 卡卡
    May 7th, 2019 at 05:27 pm
    
    @AIHGF
    
    共同学习，谢谢博主的博客，很赞！
    
    Reply
    
    AIHGF
    May 8th, 2019 at 08:37 am
    
    @卡卡
    
    多谢多谢. 一点一点的积累.
    
    Reply
leexf
April 30th, 2019 at 07:07 pm

BiSeNet的作者changqianyu在他的repo https://github.com/ycszen/TorchSeg 里实现了BiSeNet

Reply
1. AIHGF
  May 2nd, 2019 at 10:55 am
  
  @leexf
  
  非常感谢，已添加.
  
  Reply
MasterQ
April 29th, 2019 at 11:03 am

博主真棒啊，跟着博主这篇博文省了不少找论文的时间，写毕业论文全靠博主的这篇博客了！
OωO

Reply
1. AIHGF
  April 29th, 2019 at 01:15 pm
  
  @MasterQ
  
  非常荣幸能有稍许帮助，欢迎互相交流学习.
  
  Reply
  1. 咆哮的阿杰
    July 9th, 2019 at 09:26 pm
    
    @AIHGF
    
    博主有CSDN吗，能加个qq吗我的qq是 312358434
    
    Reply
    
    AIHGF
    July 9th, 2019 at 09:52 pm
    
    @咆哮的阿杰
    
    2258922522, CSDN 只是备用了.
    
    Reply
小恐龙不会飞
November 10th, 2018 at 03:59 pm

想请教一下，想识别脸上的眼镜口罩遮挡物应该看哪方面的呢(╹◡╹人)谢谢你

Reply
1. AIHGF
  November 10th, 2018 at 04:45 pm
  
  @小恐龙不会飞
  
  如果只是检测出眼镜口罩等物体，用目标检测 box 就可以.如果更细致的，语义分割可以.
  
  Reply
nana
October 13th, 2018 at 09:07 pm

博主好厉害，想请教博主关于场景的分割现在常用效果较好且代码公开的算法有哪些，最好是python语言的，谢谢博主！OωO

Reply
1. AIHGF
  October 14th, 2018 at 10:11 pm
  
  @nana
  
  场景分割经典入门应该有 PSPNet 和 DeepLab 系列的吧，PSPNet 是基于 Caffe 的，tensorflow 提供了 Deeplab 分割的.
  
  Reply
October 6th, 2018 at 10:03 am

博主厉害，博主有交流群吗？

Reply
1. AIHGF
  October 8th, 2018 at 08:33 am
  
  没有，就是个人记录备忘
  
  Reply
earhian
October 2nd, 2018 at 08:24 pm

谢谢博主，学习了

Reply
September 20th, 2018 at 02:30 pm

博主很厉害，博主下次什么时候更新呢？

Reply
1. AIHGF
  September 20th, 2018 at 07:07 pm
  
  如果你看到相关的，非常欢迎随时通知我，交流学习.
  
  Reply
2. AIHGF
  September 20th, 2018 at 06:56 pm
  
  不定期更新，看到相关的就会更新下
  
  Reply
御宅暴君
August 9th, 2018 at 07:25 pm

令人感到窒息的密集数量。

Reply
July 3rd, 2018 at 09:55 am

博主好厉害 :smile: :smile:

Reply
May 22nd, 2018 at 08:48 am

谢谢博主 :biggrin:

Reply