Papers with Code 아카이브

Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

5

보존된 평가 기록

6

코드

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

https://github.com/George-Ogden/Mask-RCNN

Mask R-CNN

https://github.com/GitBoSun/roomnet

RoomNet: End-to-End Room Layout Estimation

https://github.com/Gjiangtao/A-Deep-Supervised-Edge-Optimization-Algorithm-for-Salt-Body-Segmentation

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

https://github.com/Glutamat42/epistemic_ensemble_toy_dataset

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://github.com/GoNgXiAoPeNg1/caffeBVLCplus

Learning Deconvolution Network for Semantic Segmentation

https://github.com/GodPater/model_fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/Gojay001/toolkit-DeepLearning

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet

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

https://github.com/Gugan0905/steel-defect-detection

A novel Region of Interest Extraction Layer for Instance Segmentation

https://github.com/GunhoChoi/FusionNet-Pytorch

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

https://github.com/HAN-ARK/GVSS-S.A.Drone

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/HHY-ZJU/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HHousen/speaker-change-detection

End-to-end speaker segmentation for overlap-aware resegmentation

https://github.com/HXLH50K/U-Net-Transformer

U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

https://github.com/HYOJINPARK/C3SINet

SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder

https://github.com/HadiZayer/UNet_pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HanYong201308/Pytorch-UNet2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HaoJunNM/Unet-PretrainedMobilenetV2encoder-Flaskapp

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HarshKhandelwal1552/Language_classifier_with_Naive_Bayes

Neural Machine Translation of Rare Words with Subword Units

https://github.com/Harsharma2308/PoseRefinement

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/Hayashi-Yudai/ML_models

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hedgehogues/HoChiMinh

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hernandope/keras_resnet_FPN_cifar10

Panoptic Feature Pyramid Networks

https://github.com/HiLab-git/MIDeepSeg

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

https://github.com/HibaKob/MicroBundlePillarTrack

Segment Anything

https://github.com/HimBegginer/test_liver

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/Hoang-it/orhcid_classification

YOLACT: Real-time Instance Segmentation

https://github.com/Holmes1204/PyTorch_Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hong-Jeongmin/OC-for-smart-factory

Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

https://github.com/HongyangGao/PixelTCN

Pixel Deconvolutional Networks

https://github.com/HqWei/Semantic-Segmentation-with-Full-Convolutional-Neural-Network

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Hsankesara/DeepResearch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Huiyu-Li/Three-stage-Curriculum-Learning

A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation

https://github.com/HuseyinAfser/A-Baseline-Statistical-Method-For-Robust-User-Assisted-Multiple-Segmentation

A Baseline Statistical Method For Robust User-Assisted Multiple Segmentation

https://github.com/HussainGhazaly/PINet-master

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

https://github.com/HuynhThanhQuan/skin-detective

Microsoft COCO: Common Objects in Context

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