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/justld/nnunet_paddle

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

https://github.com/justusschock/shape-constrained-network

Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters

https://github.com/jvanvugt/pytorch-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/jwyang/graph-rcnn.pytorch

Graph R-CNN for Scene Graph Generation

https://github.com/jylins/core-text

Mask R-CNN

https://github.com/jzbjyb/rri_match

Multi-Dimensional Recurrent Neural Networks

https://github.com/jzwqaq/monodepth_jzw

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/kai422/iart

Accelerating Video Object Segmentation with Compressed Video

https://github.com/kaichoulyc/course_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kaichoulyc/tgs-salts

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

https://github.com/kakaoenterprise/Learning-Debiased-Disentangled

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

https://github.com/kaland313/vitmav45-ShipSeakers

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kaletap/language-style-transfer-pytorch

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/kaletap/nlp-experiments

A Probabilistic Formulation of Unsupervised Text Style Transfer

https://github.com/kaletap/nlp-style-transfer

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/kannyjyk/Nested-UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/karanjsingh/Improved-CSRNet

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

https://github.com/karanysingh/SIRR-using-IBCLN

Single Image Reflection Removal through Cascaded Refinement

https://github.com/karaposu/facexformer-pipeline

FaceXFormer: A Unified Transformer for Facial Analysis

https://github.com/karolzak/keras-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kartik-3004/facexbench

SegFace: Face Segmentation of Long-Tail Classes

https://github.com/kawausomando/green_vegetation_v2

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/kazuto1011/deeplab-pytorch

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

https://github.com/kazuto1011/pspnet-pytorch

Pyramid Scene Parsing Network

https://github.com/kbardool/mrcnn3

Mask R-CNN

https://github.com/kbraunlich/contort_DNN

Adapting Deep Network Features to Capture Psychological Representations

https://github.com/kc-santosh/kc-santosh.github.io

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

https://github.com/kcang2/Prune_U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kdethoor/panoptictorch

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

https://github.com/kdha0727/cancer-instance-segmentation-from-tissue

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kdhingra307/temp

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/keb123keb/deeplabv2

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

https://github.com/kekeller/semantic_soy_deeplabv3plus

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/kelvin-jose/UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kenkai21/Image_Captioning

Microsoft COCO: Common Objects in Context

https://github.com/keras-team/keras-io/blob/master/examples/vision/patch_convnet.py

Augmenting Convolutional networks with attention-based aggregation

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