1. Semantic Segmentation

  1. RGPNet: A Real-Time General Purpose Semantic Segmentation - 2019 <Paper>
  2. Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation - 2019 <Paper>
  3. Document Structure Extraction for Forms using Very High Resolution Semantic Segmentation - 2019 - Adobe 文档结构提取 <Paper>
  4. Class-Conditional Domain Adaptation on Semantic Segmentation - 2019 <Paper>
  5. On Symbiosis of Attribute Prediction and Semantic Segmentation - 2019 <Paper>
  6. Differentiable Meta-learning Model for Few-shot Semantic Segmentation - 2019 <Paper>
  7. Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach - 2019 <Paper>
  8. Real-Time Semantic Segmentation via Multiply Spatial Fusion Network - 2019 旷视 <Paper>
  9. Improving Semantic Segmentation of Aerial Images Using Patch-based Attention - 2019 <Paper>
  10. Location-aware Upsampling for Semantic Segmentation - 2019 - CAS <Paper> <Code-PyTorch>
  11. Knowledge Distillation for Incremental Learning in Semantic Segmentation - 2019 <Paper>
  12. Eye Semantic Segmentation with A Lightweight Model - 2019 眼部分割 <Paper>
  13. Distilling Pixel-Wise Feature Similarities for Semantic Segmentation - 2019 <Paper>
  14. PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation - 2019 道路分割 <Paper>
  15. Multi-source Domain Adaptation for Semantic Segmentation - 2019 - NeurIPS <Paper> <Code-Tensorflow>
  16. Region Mutual Information Loss for Semantic Segmentation - 2019 - NeurIPS <Paper> <Code-PyTorch>
  17. Correlation Maximized Structural Similarity Loss for Semantic Segmentation - 2019 <Paper>
  18. Deep Semantic Segmentation of Natural and Medical Images: A Review - 2019 医学图像分割综述 <Paper>
  19. CNN-based Semantic Segmentation using Level Set Loss - 2019 KAIST <Paper>
  20. Domain Adaptation for Semantic Segmentation with Maximum Squares Loss - 2019 <Paper> <Code-PyTorch>
  21. Distributed Iterative Gating Networks for Semantic Segmentation - 2019 <Paper>
  22. Adaptive Class Weight based Dual Focal Loss for Improved Semantic Segmentation - 2019 <Paper>
  23. Object-Contextual Representations for Semantic Segmentation - 2019 <Paper>
  24. ACFNet: Attentional Class Feature Network for Semantic Segmentation - 2019 <Paper>
  25. Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation -2019 <Paper>
  26. Feature Pyramid Encoding Network for Real-time Semantic Segmentation - 2019 <Paper>
  27. Graph-guided Architecture Search for Real-time Semantic Segmentation - 2019 <Paper>
  28. Dual Graph Convolutional Network for Semantic Segmentation - 2019 <Paper>
  29. Squeeze-and-Attention Networks for Semantic Segmentation - 2019 <Paper>
  30. Semantic Segmentation of Panoramic Images Using a Synthetic Dataset - 2019 <Paper> <Code-Github>
  31. See More Than Once – Kernel-Sharing Atrous Convolution for Semantic Segmentation - 2019 <Paper>
  32. Feedbackward Decoding for Semantic Segmentation - 2019 <Paper>
  33. Asymmetric Non-local Neural Networks for Semantic Segmentation - 2019 - ICCV <Paper> <Code-PyTorch>
  34. PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment - 2019 <Paper>
  35. Semi-Supervised Semantic Segmentation with High- and Low-level Consistency - 2019 <Paper>
  36. Benchmarking the Robustness of Semantic Segmentation Models - 2019 <Paper> <Homepage>
  37. Distance Map Loss Penalty Term for Semantic Segmentation - 2019 <Paper>
  38. I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation - 2019 <Paper>
  39. SqueezeNAS: Fast neural architecture search for faster semantic segmentation - 2019 - DeepScale <Paper>
  40. Expectation-Maximization Attention Networks for Semantic Segmentation - 2019 <Paper>
  41. Incremental Learning Techniques for Semantic Segmentation - 2019 <Paper>
  42. Interlaced Sparse Self-Attention for Semantic Segmentation - 2019 <Paper>
  43. DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation - 2019 <Paper>
  44. DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation - 2019 <Paper> <Code-PyTorch>
  45. Cross Attention Network for Semantic Segmentation - 2019 <Paper>
  46. Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks - 2019 <Paper>
  47. Efficient Segmentation: Learning Downsampling Near Semantic Boundaries - 2019 <Paper>
  48. Improving Semantic Segmentation via Dilated Affinity - 2019 <Paper>
  49. Adaptive Context Encoding Module for Semantic Segmentation - 2019 <Paper>
  50. A Regularized Convolutional Neural Network for Semantic Image Segmentation - 2019 <Paper>
  51. Deep Learning-Based Semantic Segmentation of Microscale Objects - 2019 生物细胞分割 <Paper>
  52. SAN: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images - 2019 高分辨率卫星图像分割 <Paper>
  53. Hard Pixels Mining: Learning Using Privileged Information for Semantic Segmentation - 2019 <Paper>
  54. ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation - 2019 <Paper>
  55. IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things - 2019 <Paper>
  56. Universal Barcode Detector via Semantic Segmentation - 2019 条形码分割 <Paper>
  57. Cross-view Semantic Segmentation for Sensing Surroundings - 2019 <Paper> <Code-PyTorch> <Project>
  58. Zero-Shot Semantic Segmentation - 2019 - NeurlPS <Paper> <Code-Github>
  59. Implicit Background Estimation for Semantic Segmentation - 2019 <Paper> <Code-PyTorch>
  60. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation - 2019 - NVIDIA <Paper> <Project>
  61. FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation - 2019 <Paper> <Project> <Code-PyTorch>
  62. Structured Knowledge Distillation for Semantic Segmentation - CVPR2019 <Paper>
  63. Co-Occurrent Features in Semantic Segmentation - CVPR2019 <Paper>
  64. Semantic Projection Network for Zero- and Few-Label Semantic Segmentation - CVPR2019 <Paper>
  65. Context-Reinforced Semantic Segmentation - CVPR2019 <Paper>
  66. SwiftNet - In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images - CVPR2019 <Paper> <Code-PyTorch>
  67. All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation - CVPR2019 <Paper>
  68. Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection - CVPR2019 <Paper>
  69. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach - CVPR2019 <Paper>
  70. Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation - CVPR2019 <Paper>
  71. Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation - CVPR2019 <Paper>
  72. Geometry-Aware Distillation for Indoor Semantic Segmentation - CVPR2019 <Paper>
  73. Seamless Scene Segmentation - CVPR2019 <Paper>
  74. ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation - CVPR2019 <Paper> <Code-Github>
  75. Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation - CVPR2019 <Paper>
  76. PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding - CVPR2019 <Paper> <Homepage>
  77. A Cross-Season Correspondence Dataset for Robust Semantic Segmentation - CVPR2019 <Paper>
  78. DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation-Megvii-2019 <Paper>
  79. DADA: Depth-aware Domain Adaptation in Semantic Segmentation - 2019 <Paper>
  80. GFF: Gated Fully Fusion for Semantic Segmentation - 2019 <Paper>
  81. DSNet: An Efficient CNN for Road Scene Segmentation - 2019 <Paper>
  82. FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference - CVPR2019 <Paper>
  83. An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions - 2019 <Paper>
  84. Fast-SCNN: Fast Semantic Segmentation Network - 2019 <Paper>
  85. Data augmentation using learned transforms for one-shot medical image segmentation - CVPR2019 <Paper>
  86. MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation - 2019 <Paper>
  87. CCNet: Criss-Cross Attention for Semantic Segmentation - 2018 <Paper> <Code-PyTorch>
  88. A PyTorch Semantic Segmentation Toolbox - 2018 <Paper> <Code-PyTorch>
  89. ShelfNet for Real-time Semantic Segmentation - 2018 <Paper> <Code-PyTorch>
  90. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training - ECCV2018 <Paper> <Project> <Code-MXNet>
  91. Searching for Efficient Multi-Scale Architectures for Dense Image Prediction - 2018 - Deeplab <Paper> <Code-Deeplab-Tensorflow>
  92. Light-Weight RefineNet for Real-Time Semantic Segmentation - bmvc2018 <Paper> <Code-Torch>
  93. Dual Attention Network for Scene Segmentation - 2018 <Paper>
  94. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation - ECCV 2018 - Face++ <Paper> <Code-PyTorch>
  95. Adaptive Affinity Field for Semantic Segmentation - ECCV2018 <Paper> <HomePage>
  96. Recurrent Iterative Gating Networks for Semantic Segmentation - WACV2019 <Paper>
  97. Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation - CVPR2018 <Paper>
  98. DenseASPP for Semantic Segmentation in Street Scenes - CVPR2018 <Paper> <Code-PyTorch>
  99. Pyramid Attention Network for Semantic Segmentation - 2018 - Face++ <Paper>
  100. Autofocus Layer for Semantic Segmentation - 2018 <Paper <Code-PyTorch>
  101. ExFuse: Enhancing Feature Fusion for Semantic Segmentation - ECCV2018 - Face++ <Paper>
  102. DifNet: Semantic Segmentation by Diffusion Networks - 2018 <Paper>
  103. Convolutional CRFs for Semantic Segmentation - 2018 <Paper><Code-PyTorch>
  104. ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time - 2018 <Paper>
  105. Learning a Discriminative Feature Network for Semantic Segmentation - CVPR2018 - Face++ <Paper>
  106. Vortex Pooling: Improving Context Representation in Semantic Segmentation - 2018 <Paper>
  107. Fully Convolutional Adaptation Networks for Semantic Segmentation - CVPR2018 <Paper>
  108. A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation - 2018 <Paper>
  109. Context Encoding for Semantic Segmentation - 2018 <Paper> <Code-PyTorch> <Code-PyTorch2> <Slides>
  110. ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation - ECCV2018 <Paper> <Code-Pytorch>
  111. Dynamic-structured Semantic Propagation Network - 2018 - CMU <Paper>
  112. ShuffleSeg: Real-time Semantic Segmentation Network-2018 <Paper> <Code-TensorFlow>
  113. RTSeg: Real-time Semantic Segmentation Comparative Study - 2018 <Paper> <Code-TensorFlow>
  114. Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation - 2018 <Paper>
  115. DeepLabV3+:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - 2018 - Google <Paper> <Code-Tensorflow> <Code-Karas>
  116. Adversarial Learning for Semi-Supervised Semantic Segmentation - 2018 <Paper> <Code-PyTorch>
  117. Locally Adaptive Learning Loss for Semantic Image Segmentation - 2018 <Paper>
  118. Learning to Adapt Structured Output Space for Semantic Segmentation - 2018 <Paper>
  119. Improved Image Segmentation via Cost Minimization of Multiple Hypotheses - 2018 <Paper> <Code-Matlab>
  120. TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation - 2018 - Kaggle <Paper> <Code-PyTorch> <Kaggle-Carvana Image Masking Challenge>
  121. Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation - 2018 - Google <Paper>
  122. End-to-end Detection-Segmentation Network With ROI Convolution - 2018 <Paper>
  123. Mix-and-Match Tuning for Self-Supervised Semantic Segmentation - AAAI2018 <Project> <Paper> <Code-Caffe>
  124. Learning to Segment Every Thing-2017 <Paper> <Code-Caffe2> <Code-PyTorch>
  125. Deep Dual Learning for Semantic Image Segmentation-2017 <Paper>
  126. Scene Parsing with Global Context Embedding - ICCV2017 <Paper>
  127. FoveaNet: Perspective-aware Urban Scene Parsing - ICCV2017 <Paper>
  128. Segmentation-Aware Convolutional Networks Using Local Attention Masks - 2017 <Paper> <Code-Caffe> <Project>
  129. Stacked Deconvolutional Network for Semantic Segmentation-2017 <Paper>
  130. Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF - CVPR2017 <Paper> <Caffe-Code>
  131. BlitzNet: A Real-Time Deep Network for Scene Understanding-2017 <Project> <Code-Tensorflow> <Paper>
  132. Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation -2017 <Paper> <Code-Caffe>
  133. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation - 2017 <Paper> <Code-Torch>
  134. Rethinking Atrous Convolution for Semantic Image Segmentation-2017(DeeplabV3) <Paper>
  135. Learning Object Interactions and Descriptions for Semantic Image Segmentation-2017 <Paper>
  136. Pixel Deconvolutional Networks-2017 <Code-Tensorflow> <Paper>
  137. Dilated Residual Networks-2017 <Paper> <Code-PyTorch>
  138. Recurrent Scene Parsing with Perspective Understanding in the Loop - 2017 <Project> <Paper> <Code-MatConvNet>
  139. A Review on Deep Learning Techniques Applied to Semantic Segmentation-2017 <Paper>
  140. BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks <Paper>
  141. Efficient ConvNet for Real-time Semantic Segmentation - 2017 <Paper>
  142. ICNet for Real-Time Semantic Segmentation on High-Resolution Images-2017 <Project> <Code-Caffe> <Paper> <Video>
  143. Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade-2017 <Paper> <Poster> <Project> <Code-Caffe> <Slides>
  144. Loss Max-Pooling for Semantic Image Segmentation-2017 <Paper>
  145. Annotating Object Instances with a Polygon-RNN-2017 <Project> <Paper>
  146. Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation-2017 <Project> <Code-Torch7>
  147. Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation-2017 <Paper>
  148. Adversarial Examples for Semantic Image Segmentation-2017 <Paper>
  149. Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network-2017 <Paper>
  150. Label Refinement Network for Coarse-to-Fine Semantic Segmentation-2017 <Paper>
  151. PixelNet: Representation of the pixels, by the pixels, and for the pixels-2017 <Project> <Code-Caffe> <Paper>
  152. LabelBank: Revisiting Global Perspectives for Semantic Segmentation-2017 <Paper>
  153. Progressively Diffused Networks for Semantic Image Segmentation-2017 <Paper>
  154. Understanding Convolution for Semantic Segmentation-2017 <Model-Mxnet> <Mxnet-Code> <Paper>
  155. Predicting Deeper into the Future of Semantic Segmentation-2017 <Paper>
  156. Pyramid Scene Parsing Network-2017 <Project> <Code-Caffe> <Paper> <Slides>
  157. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation-2016 <Paper>
  158. FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics-2016 <Code-PyTorch> <Paper>
  159. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation-2016 <Code-MatConvNet> <Paper> <Code-Pytorch>
  160. Learning from Weak and Noisy Labels for Semantic Segmentation - 2017 <Paper>
  161. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation <Code-Theano> <Code-Keras1> <Code-Keras2> <Paper>
  162. Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes <Code-Theano> <Paper>
  163. PixelNet: Towards a General Pixel-level Architecture-2016 <Paper>
  164. Recalling Holistic Information for Semantic Segmentation-2016 <Paper>
  165. Semantic Segmentation using Adversarial Networks-2016 <Paper> <Code-Chainer>
  166. Region-based semantic segmentation with end-to-end training-2016 <Paper>
  167. Exploring Context with Deep Structured models for Semantic Segmentation-2016 <Paper>
  168. Better Image Segmentation by Exploiting Dense Semantic Predictions-2016 <Paper>
  169. Boundary-aware Instance Segmentation-2016 <Paper>
  170. Improving Fully Convolution Network for Semantic Segmentation-2016 <Paper>
  171. Deep Structured Features for Semantic Segmentation-2016 <Paper>
  172. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs-2016 <Project> <Code-Caffe> <Code-Tensorflow> <Code-PyTorch> <Paper>
  173. DeepLab: Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs-2014 <Code-Caffe1> <Code-Caffe2> <Paper>
  174. Deep Learning Markov Random Field for Semantic Segmentation-2016 <Project> <Paper>
  175. Convolutional Random Walk Networks for Semantic Image Segmentation-2016 <Paper>
  176. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016 <Code-Caffe1> <Code-Caffe2> <Paper> <Blog>
  177. High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks-2016 <Paper>
  178. ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation-2016 <Paper>
  179. Object Boundary Guided Semantic Segmentation-2016 <Code-Caffe> <Paper>
  180. Segmentation from Natural Language Expressions-2016 <Project> <Code-Tensorflow> <Code-Caffe> <Paper>
  181. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation-2016 <Code-Caffe> <Paper>
  182. Global Deconvolutional Networks for Semantic Segmentation-2016 <Paper> <Code-Caffe>
  183. Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network-2015 <Project> <Code-Caffe> <Paper>
  184. Learning Dense Convolutional Embeddings for Semantic Segmentation-2015 <Paper>
  185. ParseNet: Looking Wider to See Better-2015 <Code-Caffe> <Model-Caffe> <Paper>
  186. Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation-2015 <Project> <Code-Caffe> <Paper>
  187. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation-2015 <Project> <Code-Caffe> <Paper> <Tutorial1> <Tutorial2>
  188. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling-2015 <Code-Caffe> <Code-Chainer> <Paper>
  189. Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform-2015 <Paper>
  190. Semantic Segmentation with Boundary Neural Fields-2015 <Code-Matlab> <Paper>
  191. Semantic Image Segmentation via Deep Parsing Network-2015 <Project> <Paper1> <Paper2> <Slides>
  192. What’s the Point: Semantic Segmentation with Point Supervision-2015 <Project> <Code-Caffe> <Model-Caffe> <Paper>
  193. U-Net: Convolutional Networks for Biomedical Image Segmentation-2015 <Project> <Code+Data> <Code-Keras> <Code-Tensorflow> <Paper> <Notes>
  194. Learning Deconvolution Network for Semantic Segmentation(DeconvNet)-2015 <Project> <Code-Caffe> <Paper> <Slides>
  195. Multi-scale Context Aggregation by Dilated Convolutions-2015 <Project> <Code-Caffe> <Code-Keras> <Paper> <Notes>
  196. ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation-2015 <Code-Theano> <Paper>
  197. BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation-2015 <Paper>
  198. Feedforward semantic segmentation with zoom-out features-2015 <Code-Torch> <Paper> <Video>
  199. Conditional Random Fields as Recurrent Neural Networks-2015 <Project> <Code-Caffe1> <Code-Caffe2> <Demo> <Paper1> <Paper2>
  200. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation-2015 <Paper>
  201. Fully Convolutional Networks for Semantic Segmentation-2015 <Code-Caffe> <Model-Caffe> <Code-Tensorflow1> <Code-Tensorflow2> <Code-Chainer> <Code-PyTorch> <Paper1> <Paper2> <Slides1> <Slides2>
  202. Deep Joint Task Learning for Generic Object Extraction-2014 <Project> <Code-Caffe> <Dataset> <Paper>
  203. Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification-2014 <Code-Caffe> <Paper>

2. Panoptic Segmentation

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

3. Human Parsing

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

4. 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-Matlab> <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>

5. Instance Segmentation

  1. SOLO: Segmenting Objects by Locations - 2019 - ByteDance <Paper>
  2. YOLACT: Real-time Instance Segmentation - 2019 <Paper> <Code-PyTorch>
  3. Pose2Seg: Detection Free Human Instance Segmentation - CVPR2019 <Paper> <Code-PyTorch> <Project> <Dataset>
  4. Mask Scoring R-CNN - CVPR2019 <Paper> <Code-PyTorch>
  5. Actor-Critic Instance Segmentation - CVPR2019 <Paper>
  6. TensorMask: A Foundation for Dense Object Segmentation - FAIR <Paper>
  7. A Pyramid CNN for Dense-Leaves Segmentation - 2018 <Paper>
  8. Predicting Future Instance Segmentations by Forecasting Convolutional Features - 2018 <Paper>
  9. Path Aggregation Network for Instance Segmentation - CVPR2018 <Paper> <Code-PyTorch>
  10. PixelLink: Detecting Scene Text via Instance Segmentation - AAAI2018 <Code-Tensorflow> <Paper>
  11. MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features - 2017 - google <Paper>
  12. Recurrent Neural Networks for Semantic Instance Segmentation-2017 <Paper>
  13. Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 <Paper>
  14. Semantic Instance Segmentation via Deep Metric Learning-2017 <Paper>
  15. Mask R-CNN-2017 <Code-Tensorflow> <Paper> <Code-Caffe2> <Code-Karas> <Code-PyTorch> <Code-MXNet>
  16. Pose2Seg: Human Instance Segmentation Without Detection - 2018 <Paper>
  17. Pose2Instance: Harnessing Keypoints for Person Instance Segmentation-2017 <Paper>
  18. Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017 <Paper>
  19. Semantic Instance Segmentation with a Discriminative Loss Function-2017 <Paper>
  20. Fully Convolutional Instance-aware Semantic Segmentation-2016 <Code-MXNet> <Paper>
  21. End-to-End Instance Segmentation with Recurrent Attention <Paper> <Code-Tensorflow>
  22. Instance-aware Semantic Segmentation via Multi-task Network Cascades-2015 <Code-Caffe> <Paper>
  23. Recurrent Instance Segmentation-2015 <Project> <Code-Torch7> <Paper> <Poster> <Video>

6. Segment Object Candidates

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

7. Foreground Object Segmentation

  1. Pixel Objectness-2017 <Project> <Code-Caffe> <Paper>
  2. A Deep Convolutional Neural Network for Background Subtraction-2017 <Paper>
Last modification:December 11th, 2019 at 05:43 pm