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/LinearPi/OCR_Chinese

COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

https://github.com/Liuhongzhi2018/Medical-Images

Medical Image Segmentation Using Deep Learning: A Survey

https://github.com/Liuhongzhi2018/Medical_images_research

Medical Image Segmentation Using Deep Learning: A Survey

https://github.com/Liumihan/CRNN_kreas

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

https://github.com/Liumihan/CRNN_pytorch

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

https://github.com/LiwenWang919/M3-UDA

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

https://github.com/Lkruitwagen/remote-sensing-solar-pv

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LoopTactics/tc-cim

Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions

https://github.com/Lopezurrutia/DSB_2018

Mask R-CNN

https://github.com/Louis24/Segmentation

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/LuanAdemi/VisualGo

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LucaHermes/graph-unet-traffic-prediction

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Luckygyana/Invo-AI

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

https://github.com/Luoxd1996/DTC

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

https://github.com/Lxrd-AJ/Advanced_ML

Fully Convolutional Networks for Semantic Segmentation

https://github.com/MASILab/SSNet

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks

https://github.com/MECLabTUDA/OOD-Gen

How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation?

https://github.com/MIC-DKFZ/DetectionAndRegression

Mask R-CNN

https://github.com/MIC-DKFZ/RegRCNN

Mask R-CNN

https://github.com/MISTLab/PEACE

Dynamic Open Vocabulary Enhanced Safe-landing with Intelligence (DOVESEI)

https://github.com/MIT-SPARK/FUSES

Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization

https://github.com/MIT-SPARK/Kimera-VIO

Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs

https://github.com/MR3z4/SemanticSegmentation

Self-Correction for Human Parsing

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/densenet

DenseNet approach to segmentation and classification of dermatoscopic skin lesions images

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vgg

Places205-VGGNet Models for Scene Recognition

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/MS-Mind/MS-Code-06/tree/main/OSVOS

One-Shot Video Object Segmentation

https://github.com/MS-Mind/MS-Code-06/tree/main/PSPNet

Pyramid Scene Parsing Network

https://github.com/MS-Mind/MS-Code-06/tree/main/nnUNet

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

https://github.com/MS-Mind/MS-Code-08/tree/main/MGN

MGNet: Monocular Geometric Scene Understanding for Autonomous Driving

https://github.com/MS-Mind/MS-Code-08/tree/main/MIMO-UNet

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

https://github.com/MS-Mind/MS-Code-08/tree/main/nnUNet

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

https://github.com/MS-P3/code7/tree/main/upernet

Unified Perceptual Parsing for Scene Understanding

https://github.com/MaczekO/AttentionNetworkProject

FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation

https://github.com/Magicboomliu/Accelerator-Simple-Template

Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

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