Segmentation

Published: 09 Oct 2015 Category: deep_learning

Papers

Deep Joint Task Learning for Generic Object Extraction

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

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

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

Nighttime sky/cloud image segmentation

Distantly Supervised Road Segmentation

Learning to Segment Human by Watching YouTube

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

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

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

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

MPF-RNN

Multi-Path Feedback Recurrent Neural Network for Scene Parsing

Scene Labeling using Recurrent Neural Networks with Explicit Long Range Contextual Dependency

PSPNet

Pyramid Scene Parsing Network

Open Vocabulary Scene Parsing

https://arxiv.org/abs/1703.08769

Deep Contextual Recurrent Residual Networks for Scene Labeling

https://arxiv.org/abs/1704.03594

Fast Scene Understanding for Autonomous Driving

  • intro: Published at “Deep Learning for Vehicle Perception”, workshop at the IEEE Symposium on Intelligent Vehicles 2017
  • arxiv: https://arxiv.org/abs/1708.02550

FoveaNet: Perspective-aware Urban Scene Parsing

https://arxiv.org/abs/1708.02421

BlitzNet: A Real-Time Deep Network for Scene Understanding

Semantic Foggy Scene Understanding with Synthetic Data

https://arxiv.org/abs/1708.07819

Benchmarks

MIT Scene Parsing Benchmark

Semantic Understanding of Urban Street Scenes: Benchmark Suite

https://www.cityscapes-dataset.com/benchmarks/

Challenges

Large-scale Scene Understanding Challenge

Places2 Challenge

http://places2.csail.mit.edu/challenge.html

Human Parsing

Human Parsing with Contextualized Convolutional Neural Network

Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing

Segmentation From Video

Fast object segmentation in unconstrained video

Recurrent Fully Convolutional Networks for Video Segmentation

Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation

Clockwork Convnets for Video Semantic Segmentation

STFCN: Spatio-Temporal FCN for Semantic Video Segmentation

One-Shot Video Object Segmentation

Video Object Segmentation Without Temporal Information

https://arxiv.org/abs/1709.06031

Convolutional Gated Recurrent Networks for Video Segmentation

Learning Video Object Segmentation from Static Images

Semantic Video Segmentation by Gated Recurrent Flow Propagation

FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos

Unsupervised learning from video to detect foreground objects in single images

https://arxiv.org/abs/1703.10901

Semantically-Guided Video Object Segmentation

https://arxiv.org/abs/1704.01926

Learning Video Object Segmentation with Visual Memory

https://arxiv.org/abs/1704.05737

Flow-free Video Object Segmentation

https://arxiv.org/abs/1706.09544

Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

https://arxiv.org/abs/1706.09364

Video Object Segmentation using Tracked Object Proposals

Video Object Segmentation with Re-identification

Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

Challenge

DAVIS: Densely Annotated VIdeo Segmentation

DAVIS Challenge on Video Object Segmentation 2017

http://davischallenge.org/challenge2017/publications.html

Projects

TF Image Segmentation: Image Segmentation framework

KittiSeg: A Kitti Road Segmentation model implemented in tensorflow.

Semantic Segmentation Architectures Implemented in PyTorch

PyTorch for Semantic Segmentation

https://github.com/ZijunDeng/pytorch-semantic-segmentation

3D Segmentation

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks

https://arxiv.org/abs/1703.03098

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

SEGCloud: Semantic Segmentation of 3D Point Clouds

Leaderboard

Segmentation Results: VOC2012 BETA: Competition “comp6” (train on own data)

http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?cls=mean&challengeid=11&compid=6

Blogs

Deep Learning for Natural Image Segmentation Priors

http://cs.brown.edu/courses/csci2951-t/finals/ghope/

Image Segmentation Using DIGITS 5

https://devblogs.nvidia.com/parallelforall/image-segmentation-using-digits-5/

Image Segmentation with Tensorflow using CNNs and Conditional Random Fields http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/18/image-segmentation-with-tensorflow-using-cnns-and-conditional-random-fields/

Fully Convolutional Networks (FCNs) for Image Segmentation

Image segmentation with Neural Net

A 2017 Guide to Semantic Segmentation with Deep Learning

http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review

Talks

Deep learning for image segmentation