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/geodekid/frnn

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

https://github.com/georgeyiasemis/U-Net-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/geox-lab/cmn

Learning without Forgetting

https://github.com/gerardrbentley/Pytorch-U-Net-AutoEncoder

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gfoxx29/Image-Captioning

Microsoft COCO: Common Objects in Context

https://github.com/giannifranchi/deeplabv3-superpixelmix

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/gift-surg/MONAIfbs

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

https://github.com/giovanniguidi/deeplabV3-PyTorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/giovanniguidi/deeplabV3_Pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/githubharald/simplehtr

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/gittar/tensorflow_transfer

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/gjp1203/LIV360SV

The Cityscapes Dataset for Semantic Urban Scene Understanding

https://github.com/gligen/GLIGEN

LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing

https://github.com/gmarceca/UTime-PlasmaStates

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

https://github.com/gmatasci/SemanticSegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/godspeedcurry/lung-nodule-detection

Mask R-CNN

https://github.com/gombru/TextFCN

Improving Text Proposals for Scene Images with Fully Convolutional Networks

https://github.com/gomezzz/distmsmatch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/google-research/deeplab2

Waymo Open Dataset: Panoramic Video Panoptic Segmentation

https://github.com/google/ffn

Flood-Filling Networks

https://github.com/gorjida/ap-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gouthamvgk/coreml_conversion_hub

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/goyalyash1224/Nuclei-detection-and-segmentaion

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/greatsharma/DeepLearning-Papers-Implementation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gregbugaj/unet-reference-impl

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/griffbr/ODMD

Learning Object Depth from Camera Motion and Video Object Segmentation

https://github.com/griffbr/VOSVS

Tukey-Inspired Video Object Segmentation

https://github.com/guanfuchen/py-faster-rcnn

Mask R-CNN

https://github.com/guanfuchen/video_obj

Fast and Accurate Online Video Object Segmentation via Tracking Parts

https://github.com/guglielmocamporese/hands-segmentation-pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/gulvarol/surreal

Learning from Synthetic Humans

https://github.com/gundeep15/SCD_AI_Project

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/gunpowder78/PReMVOS

PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation

https://github.com/gvalvano/wss-multiscale-adversarial-attention-gates

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

https://github.com/gvtulder/elasticdeform

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/h3lio5/linguistic-style-transfer-pytorch

Disentangled Representation Learning for Non-Parallel Text Style Transfer

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