Semantic Segmentation Korean Review

Contributors

풀잎스쿨 14기 Semantic Segmentation 논문으로 입문하기 멤버

• 김덕주, 김소연, 김현우, 류영표, 박근범, 백장현, 심주용, 이명오, 이영석, 이은경, 이해중, 조현정

풀잎스쿨 16기 Semantic Segmentation 논문으로 입문하기 2기 멤버

• 고재영, 김덕주, 김소연, 김현우, 심주용, 이영석, 이은경, 이한얼, 이해중, 조현정, 한건우

Encoder-Decoder 형식의 Semantic Segmentation

• Fully Convolutional Networks for semantic Segmentation [paper / reivew]
• Learning Deconvolution Network for Semantic Segmentation [paper / review]
• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [paper / review]
• RedNet: Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections [paper / review]
• LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation [paper / review]
• SDN : Stacked Deconvolutional Network for Semantic Segmentation [paper / review]

U-Net 형식의 Semantic Segmentation

• U-Net: Convolutional Networks for Biomedical Image Segmentation [paper / review]
• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [paper / review]
• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation [paper1, paper2 / review]
• UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [paper / review]
• Attention U-Net: Learning Where to Look for the Pancreas [paper / review]
• The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation [paper / review]
• Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment [paper / review]
• Double-UNet: DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation [paper / review]

DeepLab 형식의 이용한 Semantic Segmentation

• Multi-Scale Context Aggregation by Dilated Convolutions [paper / review]
• DeepLab V1: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs [paper / review]
• DeepLab V2: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [paper / review]
• DeepLab V3: Rethinking Atrous Convolution for Semantic Image Segmentation [paper / review]
• DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [paper / review]

Receptive Field의 한계를 극복하려고 시도한 Semantic Segmentation

• Pyramid Scene Parsing Network [paper / review]
• Deformable Convolutional Networks [paper / review]
• Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network [paper / review]

High Resolution을 복원하려는 논문

• HRNet : Deep High-Resolution Representation Learning for Visual Recognition [paper / review]

Real-Time Semantic Segmentation

• ENet : A Deep Neural Network Architecture for Real-Time Semantic Segmentation [paper / review]
• ERFNet : Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation [paper / review]
• ICNet : ICNet for Real-Time Semantic Segmentation on High-Resolution Images [paper / review]
• BiseNetv1 : Bilateral Segmentation Network for Real-time Semantic Segmentation [paper / review]
• BiseNetv2 : Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation [paper / review]
• ESPNet : Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation [paper / review]

Competition paper

[ECCV 2020 Workshop]

• Summary [pdf]
• 1st : Multi-level tail pixel cutmix and scale attention for long-tailed scene parsing [paper]
• 2nd : Diversification is All You Need: Towards Data Efficient Image Understanding [paper]
• 3rd : Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge [paper]
• 4th : EfficientSeg: An Efficient Semantic Segmentation Network [paper]
• 5th : Data-efficient semantic segmentation via extremely perturbed data augmentation [paper]
• other : Hoya's segmentation_tutorial_pytorch [paper / code]

[SIIM-ACR Pneumothorax Segmentation]

• Summary [pdf]

[Severstal Steel Defect Detection]

• Summary [pdf]

[Kaggle Summary Poster]

• https://neptune.ai/blog/image-segmentation-tips-and-tricks-from-kaggle-competitions