Papers with Code 아카이브

2D Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

17

평가표

11

보존된 평가 기록

42

코드

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

https://github.com/SonginCV/GMPHD_MAF

Mask R-CNN

https://github.com/SonginCV/GMPHD_SAF

Mask R-CNN

https://github.com/SonginCV/MAF_HDA

Mask R-CNN

https://github.com/SonpKing/semantic-segmentation-pytorch

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/SonwYang/building-extraction

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/Soren-Kierkegaard/MRI-Image-Analysis

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Spider-scnu/Instance-Segmentation-For-Cancer

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Srijan2001/Keypoint-regression

Real-time 3D Traffic Cone Detection for Autonomous Driving

https://github.com/StanLei52/GEBD

Generic Event Boundary Detection: A Benchmark for Event Segmentation

https://github.com/StephenEkaputra/Mask_RCNN-TinyPascalVOC

Mask R-CNN

https://github.com/Sudhir11292rt/myBDL

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

https://github.com/Suhird/AirbusShipDetection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SunDaDenny/DuLa-Net

DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama

https://github.com/Suvoo/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Syarujianai/deeplab-commented

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/SzuPc/mindspore-UnetAG

Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/T-Almeida/tensorflow-keras-multidimensional-rnn

Multi-Dimensional Recurrent Neural Networks

https://github.com/T-a-g-z/Yotube-VOS-OVS

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

https://github.com/TTMRonald/MobileNet_Zoo

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/TWJianNuo/panoptic-scene-understanding

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/TanyaChutani/DeepLabV3Plus-Tf2.0

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/TanyaChutani/Monodepth-Tf2.x

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/TanyaChutani/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Tashnim-Chowdhury/FloodNet-Challenge-EARTHVISION2021

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

https://github.com/TejasBajania/Mtech_pro

Fully Convolutional Networks for Semantic Segmentation

https://github.com/TejasBajania/Mtech_thesis_project

Fully Convolutional Networks for Semantic Segmentation

https://github.com/Tez01/SegNet-Keras-Implementation

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

https://github.com/The-AI-Summer/self-attention-cv

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

https://github.com/TheInfamousWayne/UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TheLegendAli/DeepLab-Context

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

https://github.com/TheRealBaka/Models-in-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TheTrveAnthony/no-Green

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/TheUser0815/segnet-pytorch

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

https://github.com/TheUser0815/unetpp-pytorch

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/TianyiZhang0315/CV_FinalProject

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/TimoSaemann/caffe-segnet-cudnn5

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

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