List of awesome things around semantic segmentation 🎉

Overview

Awesome Semantic Segmentation

Awesome

List of awesome things around semantic segmentation 🎉

Semantic segmentation is a computer vision task in which we label specific regions of an image according to what's being shown. Semantic segmentation awswers for the question: "What's in this image, and where in the image is it located?".

Semantic segmentation is a critical module in robotics related applications, especially autonomous driving, remote sensing. Most of the research on semantic segmentation is focused on improving the accuracy with less attention paid to computationally efficient solutions.

Seft-driving-car

The recent appoarch in semantic segmentation is using deep neural network, specifically Fully Convolutional Network (a.k.a FCN). We can follow the trend of semantic segmenation approach at: paper-with-code.

Evaluate metrics: mIOU, accuracy, speed,...

State-Of-The-Art (SOTA) methods of Semantic Segmentation

Paper Benchmark on PASALVOC12 Release Implement
EfficientNet-L2+NAS-FPN Rethinking Pre-training and Self-training 90.5% NeurIPS 2020 TF
DeepLab V3+ Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation 89% ECCV 2018 TF, Keras, Pytorch, Demo
DeepLab V3 Rethinking Atrous Convolution for Semantic Image Segmentation 86.9% 17 Jun 2017 TF, TF
Smooth Network with Channel Attention Block Learning a Discriminative Feature Network for Semantic Segmentation 86.2% CVPR 2018 Pytorch
PSPNet Pyramid Scene Parsing Network 85.4% CVPR 2017 Keras, Pytorch, Pytorch
ResNet-38 MS COCO Wider or Deeper: Revisiting the ResNet Model for Visual Recognition 84.9% 30 Nov 2016 MXNet
RefineNet RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation 84.2% CVPR 2017 Matlab, Keras
GCN Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network 83.6% CVPR 2017 TF
CRF-RNN Conditional Random Fields as Recurrent Neural Networks 74.7% ICCV 2015 Matlab, TF
ParseNet ParseNet: Looking Wider to See Better 69.8% 15 Jun 2015 Caffe
Dilated Convolutions Multi-Scale Context Aggregation by Dilated Convolutions 67.6% 23 Nov 2015 Caffe
FCN Fully Convolutional Networks for Semantic Segmentation 67.2% CVPR 2015 Caffe

Variants

  • FCN with VGG(Resnet, Densenet) backbone: pytorch
  • The easiest implementation of fully convolutional networks (FCN8s VGG): pytorch
  • TernausNet (UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset paper: pytorch
  • TernausNetV2: Fully Convolutional Network for Instance Segmentation: pytorch

Review list of Semantic Segmentation

  • Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey 2020 (University of Gour Banga,India)
  • A peek of Semantic Segmentation 2018 (mc.ai)
  • Semantic Segmentation guide 2018 (towardds)
  • An overview of semantic image segmentation (jeremyjordan.me)
  • Recent progress in semantic image segmentation 2018 (arxiv, towardsdatascience)
  • A 2017 Guide to Semantic Segmentation Deep Learning Review (blog.qure.ai)
  • Review popular network architecture (medium-towardds)
  • Lecture 11 - Detection and Segmentation - CS231n (slide, vid):
  • A Survey of Semantic Segmentation 2016 (arxiv)

Case studies

  • Dstl Satellite Imagery Competition, 3rd Place Winners' Interview: Vladimir & Sergey: Blog, Code
  • Carvana Image Masking Challenge–1st Place Winner's Interview: Blog, Code
  • Data Science Bowl 2017, Predicting Lung Cancer: Solution Write-up, Team Deep Breath: Blog
  • MICCAI 2017 Robotic Instrument Segmentation: Code and explain
  • 2018 Data Science Bowl Find the nuclei in divergent images to advance medical discovery: 1st place, 2nd, 3rd, 4th, 5th, 10th
  • Airbus Ship Detection Challenge: 4th place, 6th

Most used loss functions

  • Pixel-wise cross entropy loss:
  • Dice loss: which is pretty nice for balancing dataset
  • Focal loss:
  • Lovasz-Softmax loss:

Datasets

Frameworks for segmentation

Related techniques

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