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/Project-MONAI/MONAI

UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unetr_btcv_segmentation_3d.ipynb

UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/PuchatekwSzortach/voc_encoder_decoder_with_atrous_separable_convolutions

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/PuchatekwSzortach/voc_fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/PurabPatel555/Pytorch-UNet-Brain-MRI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Qengineering/TensorFlow-Lite-RPi-32-bit-Segmentation

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

https://github.com/Qengineering/TensorFlow-Lite-RPi-64-bit-Segmentation

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

https://github.com/Qengineering/TensorFlow_Lite_Segmentation_Jetson-Nano

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

https://github.com/Qengineering/TensorFlow_Lite_Segmentation_RPi_32-bit

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

https://github.com/Qengineering/TensorFlow_Lite_Segmentation_RPi_64-bit

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

https://github.com/QingyongHu/SQN

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

https://github.com/Qinhj07/ATOMCode

Mask R-CNN

https://github.com/RPIDIAL/SAMix

DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets

https://github.com/RTalha/CROWD-COUNTING-USING-CSRNET

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

https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection

Mask R-CNN

https://github.com/RajArPatra/Super-OCR

Mask R-CNN

https://github.com/RajkumarPreetham/Road-scene-understanding

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

https://github.com/Rakeshvd/Instance-Segmenation-of-MRI-scan-using-Mask-RCNN

Mask R-CNN

https://github.com/Rakeshvd/Semantic-Segmenation-of-MRI-scan-using-Mask-RCNN

Mask R-CNN

https://github.com/RashmiUSC/3D-RU-Net

3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

https://github.com/RemiArbache/style-transfer-M2

Text Style Transfer: A Review and Experimental Evaluation

https://github.com/Rintarooo/PSPNet

Pyramid Scene Parsing Network

https://github.com/RituYadav92/Image-segmentation

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

https://github.com/RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection

Mask R-CNN

https://github.com/Robinatp/Deeplab_Tensorflow

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/RobotMobile/cv-deep-learning-paper-review

What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

https://github.com/RogerQi/pascal-5i

One-Shot Learning for Semantic Segmentation

https://github.com/RoichatulJannah21/neural-odes-segmentation

Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

https://github.com/Rosie-Brigham/sesmeg

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/RuiZhou-cn/U3HS

Segmenting Known Objects and Unseen Unknowns without Prior Knowledge

https://github.com/RuijieZhu94/mmdepth

BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

https://github.com/RylanSchaeffer/prismatic-vlms

Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

https://github.com/S-HuaBomb/MASTER-paddle

MASTER: Multi-Aspect Non-local Network for Scene Text Recognition

https://github.com/SANKHA1/Vehicle-Detection

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

https://github.com/SINGROUP/ED-AFM

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

https://github.com/SK124/Semantic-Segmentaion-Using-UNets

U-Net: Convolutional Networks for Biomedical Image Segmentation

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