Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/IDEA-opensource/DAB-DETR

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/IDEA-opensource/DN-DETR

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/IDEACVR/DINO

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/IE-group/conference_report

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/IIGROUP/TediGAN

Towards Open-World Text-Guided Face Image Generation and Manipulation

https://github.com/IMPLabUniPr/mmdetection-groie

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/IMvision12/SegFormer-tf

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/IMvision12/keras-vision-models

Deep Residual Learning for Image Recognition

https://github.com/INK-USC/DualRE

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/IPIU-detection/SEANet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/IPMI-ICNS-UKE/multiscale

Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture

https://github.com/IRVLab/SUIM

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IRVLab/SUIM-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IVRL/MulT

MulT: An End-to-End Multitask Learning Transformer

https://github.com/Iamal1/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Iamanorange/DSSLIC

DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

https://github.com/Iammuratc/grainDetection

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

https://github.com/Ian-Liao/SegCaps

Capsules for Object Segmentation

https://github.com/IcarusWizard/MAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/IgorZaton/Mini-Projects

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IllIIIllll/where-is-wally

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

https://github.com/IlliaOvcharenko/lung-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IlyaDobrynin/GridMixup

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

https://github.com/ImagingLab/Colorizing-with-GANs

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IntelLabs/AVUC

Loss-Calibrated Approximate Inference in Bayesian Neural Networks

https://github.com/IntelligenceDatum/ICCV2019_Model_Compression

YOLACT: Real-time Instance Segmentation

https://github.com/IrohXu/lanenet-lane-detection-pytorch

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/IsaacPatole/TGS-Salt-Identification-Challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IsaacRe/splatnet

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

https://github.com/IuliiaZaitova/code_slingers

Microsoft COCO: Common Objects in Context

https://github.com/IvanVassi/LaneNet

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/IzPerfect/CT_Image_Segmentation

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

https://github.com/J-jjj7230/u-net_model

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JCZ404/Semi-DETR

End-to-End Semi-Supervised Object Detection with Soft Teacher

https://github.com/JContro/pytorch_projects

Deep Residual Learning for Image Recognition

https://github.com/JDAI-CV/CoTNet

Contextual Transformer Networks for Visual Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.