Published: 09 Oct 2015 Category: deep_learning


Deep Joint Task Learning for Generic Object Extraction

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


U-Net: Convolutional Networks for Biomedical Image Segmentation

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

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

Annotating Object Instances with a Polygon-RNN

Nighttime sky/cloud 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


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)

DeepLab v3

Rethinking Atrous Convolution for Semantic Image Segmentation


Conditional Random Fields as Recurrent Neural Networks


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

Efficient piecewise training of deep structured models for semantic segmentation


Learning Deconvolution Network for Semantic Segmentation


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: Looking Wider to See Better


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: 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: 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: 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:

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: 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:

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

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

Loss Max-Pooling for Semantic Image Segmentation

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

A Review on Deep Learning Techniques Applied to Semantic Segmentation

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


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


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

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


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:

Mask R-CNN

Semantic Instance Segmentation via Deep Metric Learning

Pose2Instance: Harnessing Keypoints for Person Instance Segmentation

Pixelwise Instance Segmentation with a Dynamically Instantiated Network

Instance-Level Salient Object 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

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


Multi-Path Feedback Recurrent Neural Network for Scene Parsing

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


Pyramid Scene Parsing Network

Open Vocabulary Scene Parsing

Deep Contextual Recurrent Residual Networks for Scene Labeling


MIT Scene Parsing Benchmark

Semantic Understanding of Urban Street Scenes: Benchmark Suite


Large-scale Scene Understanding Challenge

Places2 Challenge

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

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

Semantically-Guided Video Object Segmentation

Learning Video Object Segmentation with Visual Memory

Flow-free Video Object Segmentation

Online Adaptation of Convolutional Neural Networks for Video Object Segmentation


DAVIS: Densely Annotated VIdeo Segmentation


TF Image Segmentation: Image Segmentation framework

KittiSeg: A Kitti Road Segmentation model implemented in tensorflow.

Semantic Segmentation Architectures Implemented in PyTorch

3D Segmentation

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

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


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


Deep Learning for Natural Image Segmentation Priors

Image Segmentation Using DIGITS 5

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


Deep learning for image segmentation