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Semantic Segmentation
- Adaptive Affinity Field for Semantic Segmentation – ECCV2018
- Pyramid Attention Network for Semantic Segmentation – 2018 – Face++
- Autofocus Layer for Semantic Segmentation – 2018 [
- ExFuse: Enhancing Feature Fusion for Semantic Segmentation – 2018 – Face++
- DifNet: Semantic Segmentation by Diffusion Networks – 2018
- Convolutional CRFs for Semantic Segmentation – 2018
- ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time – 2018
- Learning a Discriminative Feature Network for Semantic Segmentation – CVPR2018 – Face++
- Vortex Pooling: Improving Context Representation in Semantic Segmentation – 2018
- Fully Convolutional Adaptation Networks for Semantic Segmentation – CVPR2018
- A Multi-Layer Approach to Superpixel-based Higher-order Conditional Random Field for Semantic Image Segmentation – 2018
- Context Encoding for Semantic Segmentation – 2018
- ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation – 2018
- Dynamic-structured Semantic Propagation Network – 2018 – CMU
- ShuffleSeg: Real-time Semantic Segmentation Network-2018
- RTSeg: Real-time Semantic Segmentation Comparative Study – 2018
- Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation – 2018
- DeepLabV3+:Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation – 2018 – Google
- Adversarial Learning for Semi-Supervised Semantic Segmentation – 2018
- Locally Adaptive Learning Loss for Semantic Image Segmentation – 2018
- Learning to Adapt Structured Output Space for Semantic Segmentation – 2018
- Improved Image Segmentation via Cost Minimization of Multiple Hypotheses – 2018
- TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation – 2018 – Kaggle
- Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation – 2018 – Google
- End-to-end Detection-Segmentation Network With ROI Convolution – 2018
- Mix-and-Match Tuning for Self-Supervised Semantic Segmentation – AAAI2018
- Learning to Segment Every Thing-2017
- Deep Dual Learning for Semantic Image Segmentation-2017
- Scene Parsing with Global Context Embedding – 2017 – ICCV
- FoveaNet: Perspective-aware Urban Scene Parsing – 2017 – ICCV
- Segmentation-Aware Convolutional Networks Using Local Attention Masks – 2017
- Stacked Deconvolutional Network for Semantic Segmentation-2017
- Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF – CVPR2017
- BlitzNet: A Real-Time Deep Network for Scene Understanding-2017
- Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation -2017
- LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation – 2017
- Rethinking Atrous Convolution for Semantic Image Segmentation-2017(DeeplabV3)
- Learning Object Interactions and Descriptions for Semantic Image Segmentation-2017
- Pixel Deconvolutional Networks-2017
- Dilated Residual Networks-2017
- Recurrent Scene Parsing with Perspective Understanding in the Loop – 2017
- A Review on Deep Learning Techniques Applied to Semantic Segmentation-2017
- BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks
- Efficient ConvNet for Real-time Semantic Segmentation – 2017
- ICNet for Real-Time Semantic Segmentation on High-Resolution Images-2017
- Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade-2017
- Loss Max-Pooling for Semantic Image Segmentation-2017
- Annotating Object Instances with a Polygon-RNN-2017
- Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation-2017
- Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation-2017
- Adversarial Examples for Semantic Image Segmentation-2017
- Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network-2017
- Label Refinement Network for Coarse-to-Fine Semantic Segmentation-2017
- PixelNet: Representation of the pixels, by the pixels, and for the pixels-2017
- LabelBank: Revisiting Global Perspectives for Semantic Segmentation-2017
- Progressively Diffused Networks for Semantic Image Segmentation-2017
- Understanding Convolution for Semantic Segmentation-2017
- Predicting Deeper into the Future of Semantic Segmentation-2017
- Pyramid Scene Parsing Network-2017
- FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation-2016
- FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics-2016
- RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation-2016
- Learning from Weak and Noisy Labels for Semantic Segmentation – 2017
- The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
- PixelNet: Towards a General Pixel-level Architecture-2016
- Recalling Holistic Information for Semantic Segmentation-2016
- Semantic Segmentation using Adversarial Networks-2016
- Region-based semantic segmentation with end-to-end training-2016
- Exploring Context with Deep Structured models for Semantic Segmentation-2016
- Better Image Segmentation by Exploiting Dense Semantic Predictions-2016
- Boundary-aware Instance Segmentation-2016
- Improving Fully Convolution Network for Semantic Segmentation-2016
- Deep Structured Features for Semantic Segmentation-2016
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs-2016
- DeepLab: Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs-2014
- Deep Learning Markov Random Field for Semantic Segmentation-2016
- Convolutional Random Walk Networks for Semantic Image Segmentation-2016
- ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation-2016
- High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks-2016
- ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation-2016
- Object Boundary Guided Semantic Segmentation-2016
- Segmentation from Natural Language Expressions-2016
- Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation-2016
- Global Deconvolutional Networks for Semantic Segmentation-2016
- Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network-2015
- Learning Dense Convolutional Embeddings for Semantic Segmentation-2015
- ParseNet: Looking Wider to See Better-2015
- Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation-2015
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation-2015
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling-2015
- Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform-2015
- Semantic Segmentation with Boundary Neural Fields-2015
- Semantic Image Segmentation via Deep Parsing Network-2015
- What’s the Point: Semantic Segmentation with Point Supervision-2015
- U-Net: Convolutional Networks for Biomedical Image Segmentation-2015
- Learning Deconvolution Network for Semantic Segmentation(DeconvNet)-2015
- Multi-scale Context Aggregation by Dilated Convolutions-2015
- ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation-2015
- BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation-2015
- Feedforward semantic segmentation with zoom-out features-2015
- Conditional Random Fields as Recurrent Neural Networks-2015
- Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation-2015
- Fully Convolutional Networks for Semantic Segmentation-2015
- Deep Joint Task Learning for Generic Object Extraction-2014
- Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification-2014
Panoptic Segmentation
- Panoptic Segmentation – 2018
Human Parsing
- Macro-Micro Adversarial Network for Human Parsing – ECCV2018
- Holistic, Instance-level Human Parsing – 2017
- Semi-Supervised Hierarchical Semantic Object Parsing – 2017
- Towards Real World Human Parsing: Multiple-Human Parsing in the Wild – 2017
- Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing-2017
- Efficient and Robust Deep Networks for Semantic Segmentation – 2017
- Deep Learning for Human Part Discovery in Images-2016
- A CNN Cascade for Landmark Guided Semantic Part Segmentation-2016
- Deep Learning for Semantic Part Segmentation With High-level Guidance-2015
- Neural Activation Constellations-Unsupervised Part Model Discovery with Convolutional Networks-2015
- Human Parsing with Contextualized Convolutional Neural Network-2015
- Part detector discovery in deep convolutional neural networks-2014
Clothes Parsing
- Looking at Outfit to Parse Clothing-2017
- Semantic Object Parsing with Local-Global Long Short-Term Memory-2015
- A High Performance CRF Model for Clothes Parsing-2014
- Clothing co-parsing by joint image segmentation and labeling-2013
- Parsing clothing in fashion photographs-2012
Instance Segmentation
- A Pyramid CNN for Dense-Leaves Segmentation – 2018
- Predicting Future Instance Segmentations by Forecasting Convolutional Features – 2018
- Path Aggregation Network for Instance Segmentation – CVPR2018
- PixelLink: Detecting Scene Text via Instance Segmentation – AAAI2018
- MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features – 2017 – google
- Recurrent Neural Networks for Semantic Instance Segmentation-2017
- Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017
- Semantic Instance Segmentation via Deep Metric Learning-2017
- Mask R-CNN-2017
- Pose2Instance: Harnessing Keypoints for Person Instance Segmentation-2017
- Pixelwise Instance Segmentation with a Dynamically Instantiated Network-2017
- Semantic Instance Segmentation with a Discriminative Loss Function-2017
- Fully Convolutional Instance-aware Semantic Segmentation-2016
- End-to-End Instance Segmentation with Recurrent Attention
- Instance-aware Semantic Segmentation via Multi-task Network Cascades-2015
- Recurrent Instance Segmentation-2015
Segment Object Candidates
- FastMask: Segment Object Multi-scale Candidates in One Shot-2016
- Learning to Refine Object Segments-2016
- Learning to Segment Object Candidates-2015
Foreground Object Segmentation
- Pixel Objectness-2017
- A Deep Convolutional Neural Network for Background Subtraction-2017
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