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語義分割深度學習方法集錦

阿新 • • 發佈：2018-11-17

Papers

Deep Joint Task Learning for Generic Object Extraction

Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

Segmentation from Natural Language Expressions

Semantic Object Parsing with Graph LSTM

Fine Hand Segmentation using Convolutional Neural Networks

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

Texture segmentation with Fully Convolutional Networks

Fast LIDAR-based Road Detection Using Convolutional Neural Networks

https://arxiv.org/abs/1703.03613

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

Annotating Object Instances with a Polygon-RNN

Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF

Nighttime sky/cloud image segmentation

Distantly Supervised Road Segmentation

Superpixel clustering with deep features for unsupervised road segmentation

Learning to Segment Human by Watching YouTube

W-Net: A Deep Model for Fully Unsupervised Image Segmentation

https://arxiv.org/abs/1711.08506

End-to-end detection-segmentation network with ROI convolution

U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation

https://arxiv.org/abs/1709.00201

TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

Foreground Object Segmentation

Pixel Objectness

A Deep Convolutional Neural Network for Background Subtraction

Semantic Segmentation

Fully Convolutional Networks for Semantic Segmentation

From Image-level to Pixel-level Labeling with Convolutional Networks

Feedforward semantic segmentation with zoom-out features

DeepLab

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

DeepLab v2

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

DeepLabv2 (ResNet-101)

http://liangchiehchen.com/projects/DeepLabv2_resnet.html

DeepLab v3

Rethinking Atrous Convolution for Semantic Image Segmentation

CRF-RNN

Conditional Random Fields as Recurrent Neural Networks

BoxSup

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

Efficient piecewise training of deep structured models for semantic segmentation

DeconvNet

Learning Deconvolution Network for Semantic Segmentation

SegNet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

SegNet: Pixel-Wise Semantic Labelling Using a Deep Networks

Getting Started with SegNet

ParseNet

ParseNet: Looking Wider to See Better

DecoupledNet

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

Semantic Image Segmentation via Deep Parsing Network

Multi-Scale Context Aggregation by Dilated Convolutions

Instance-aware Semantic Segmentation via Multi-task Network Cascades

Object Segmentation on SpaceNet via Multi-task Network Cascades (MNC)

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

ScribbleSup

ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

Laplacian Reconstruction and Refinement for Semantic Segmentation

Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction

Convolutional Random Walk Networks for Semantic Image Segmentation

ENet

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery

Deep Learning Markov Random Field for Semantic Segmentation

Region-based semantic segmentation with end-to-end training

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

PixelNet

PixelNet: Towards a General Pixel-level Architecture

Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation

  • intro: IEEE T. Image Processing
  • intro: propose an RGB-D semantic segmentation method which applies a multi-task training scheme: semantic label prediction and depth value regression
  • arxiv: https://arxiv.org/abs/1610.01706

PixelNet: Representation of the pixels, by the pixels, and for the pixels

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

Deep Structured Features for Semantic Segmentation

CNN-aware Binary Map for General Semantic Segmentation

Efficient Convolutional Neural Network with Binary Quantization Layer

Mixed context networks for semantic segmentation

High-Resolution Semantic Labeling with Convolutional Neural Networks

Gated Feedback Refinement Network for Dense Image Labeling

RefineNet

RefineNet: Multi-Path Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

Semantic Segmentation using Adversarial Networks

Improving Fully Convolution Network for Semantic Segmentation

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Training Bit Fully Convolutional Network for Fast Semantic Segmentation

Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection

  • intro: “an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation 
    with built-in awareness of semantically meaningful boundaries. “
  • arxiv: https://arxiv.org/abs/1612.01337

Diverse Sampling for Self-Supervised Learning of Semantic Segmentation

Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Understanding Convolution for Semantic Segmentation

Label Refinement Network for Coarse-to-Fine Semantic Segmentation

https://www.arxiv.org/abs/1703.00551

Predicting Deeper into the Future of Semantic Segmentation

Guided Perturbations: Self Corrective Behavior in Convolutional Neural Networks

Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade

Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network

https://arxiv.org/abs/1703.02719

Loss Max-Pooling for Semantic Image Segmentation

Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation

https://arxiv.org/abs/1704.03593

A Review on Deep Learning Techniques Applied to Semantic Segmentation

https://arxiv.org/abs/1704.06857

Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks

ICNet

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

LinkNet

Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

Pixel Deconvolutional Networks

Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges

Semantic Segmentation with Reverse Attention

Stacked Deconvolutional Network for Semantic Segmentation

https://arxiv.org/abs/1708.04943

Learning Dilation Factors for Semantic Segmentation of Street Scenes

A Self-aware Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

https://arxiv.org/abs/1709.02764

One-Shot Learning for Semantic Segmentation

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

https://arxiv.org/abs/1709.02764

Semantic Segmentation from Limited Training Data

https://arxiv.org/abs/1709.07665

Unsupervised Domain Adaptation for Semantic Segmentation with GANs

https://arxiv.org/abs/1711.06969

Neuron-level Selective Context Aggregation for Scene Segmentation

https://arxiv.org/abs/1711.08278

Road Extraction by Deep Residual U-Net

https://arxiv.org/abs/1711.10684

Mix-and-Match Tuning for Self-Supervised Semantic Segmentation

Error Correction for Dense Semantic Image Labeling

https://arxiv.org/abs/1712.03812

Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions

https://arxiv.org/abs/1801.01317

Instance Segmentation

Simultaneous Detection and Segmentation

Convolutional Feature Masking for Joint Object and Stuff Segmentation

Proposal-free Network for Instance-level Object Segmentation

Hypercolumns for object segmentation and fine-grained localization

SDS using hypercolumns

Learning to decompose for object detection and instance segmentation

Recurrent Instance Segmentation

Instance-sensitive Fully Convolutional Networks

Amodal Instance Segmentation

Bridging Category-level and Instance-level Semantic Image Segmentation

Bottom-up Instance Segmentation using Deep Higher-Order CRFs

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

End-to-End Instance Segmentation and Counting with Recurrent Attention

TA-FCN / FCIS

Translation-aware Fully Convolutional Instance Segmentation

Fully Convolutional Instance-aware Semantic Segmentation

InstanceCut: from Edges to Instances with MultiCut

Deep Watershed Transform for Instance Segmentation

Object Detection Free Instance Segmentation With Labeling Transformations

Shape-aware Instance Segmentation

Interpretable Structure-Evolving LSTM

  • intro: CMU & Sun Yat-sen University & National University of Singapore & Adobe Research
  • intro: CVPR 2017 spotlight paper
  • arxiv: https://arxiv.org/abs/1703.03055

Mask R-CNN

Semantic Instance Segmentation via Deep Metric Learning

https://arxiv.org/abs/1703.10277

Pose2Instance: Harnessing Keypoints for Person Instance Segmentation

https://arxiv.org/abs/1704.01152

Pixelwise Instance Segmentation with a Dynamically Instantiated Network

Instance-Level Salient Object Segmentation

Semantic Instance Segmentation with a Discriminative Loss Function

SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

https://arxiv.org/abs/1709.07158

S4 Net: Single Stage Salient-Instance Segmentation

Deep Extreme Cut: From Extreme Points to Object Segmentation

https://arxiv.org/abs/1711.09081

Learning to Segment Every Thing

Recurrent Neural Networks for Semantic Instance Segmentation

MaskLab

MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

https://arxiv.org/abs/1712.04837

Recurrent Pixel Embedding for Instance Grouping

Specific Segmentation

A CNN Cascade for Landmark Guided Semantic Part Segmentation

End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks

Face Parsing via Recurrent Propagation

Face Parsing via a Fully-Convolutional Continuous CRF Neural Network

https://arxiv.org/abs/1708.03736

Boundary-sensitive Network for Portrait Segmentation

https://arxiv.org/abs/1712.08675

Segment Proposal

Learning to Segment Object Candidates

Learning to Refine Object Segments

FastMask: Segment Object Multi-scale Candidates in One Shot

Scene Labeling / Scene Parsing

Indoor Semantic Segmentation using depth information

Recurrent Convolutional Neural Networks for Scene Parsing

Learning hierarchical features for scene labeling

Multi-modal unsupervised feature learning for rgb-d scene labeling

Scene Labeling with LSTM Recurrent Neural Networks

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

“Semantic Segmentation for Scene Understanding: Algorithms and Implementations” tutorial

Semantic Understanding of Scenes through the ADE20K Dataset

Learning Deep Representations for Scene Labeling with Guided Supervision

Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

MPF-RNN

Multi-Path Feedback Recurrent Neural Network for Scene Parsing

  • arxiv

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