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/2023-MindSpore-1/ms-code-222/tree/main/pointrend

PointRend: Image Segmentation as Rendering

https://github.com/2023-MindSpore-1/ms-code-224/tree/main/Alexnet

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea

Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

https://github.com/2023-MindSpore-1/ms-code-42

Fully Convolutional Networks for Semantic Segmentation

https://github.com/2023-MindSpore-1/ms-code-46

Pyramid Scene Parsing Network

https://github.com/2023-MindSpore-1/ms-code-58

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

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/cct

UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2023-MindSpore-1/ms-code-66

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-67

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/UNet3%2B

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

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tinydarknet

Mask R-CNN

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/vnet

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

https://github.com/2023-MindSpore-4/Code-5/tree/main/ibnnet

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/2023-MindSpore-4/Code1/tree/main/DeepLabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-4/Code1/tree/main/Efficientnet

EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

https://github.com/2023-MindSpore-4/Code10/tree/main/DeepLabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-4/Code10/tree/main/Efficientnet

EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

https://github.com/2023-MindSpore-4/Code10/tree/main/FCN8s

Fully Convolutional Networks for Semantic Segmentation

https://github.com/2023-MindSpore-4/Code10/tree/main/MaskRCNN

Mask R-CNN

https://github.com/2023-MindSpore-4/Code10/tree/main/Unet

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

https://github.com/2023-MindSpore-4/Code11/tree/main/SPPNet

SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation

https://github.com/2023-MindSpore-4/Code11/tree/main/Segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/2023-MindSpore-4/Code11/tree/main/SemanticHumanMatting

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/2023-MindSpore-4/Code11/tree/main/SinGAN

SinGAN-Seg: Synthetic training data generation for medical image segmentation

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/sam

Segment Anything

https://github.com/2023-MindSpore-4/Code14/tree/main/SPPNet

SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation

https://github.com/2023-MindSpore-4/Code14/tree/main/Segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/2023-MindSpore-4/Code14/tree/main/SinGAN

SinGAN-Seg: Synthetic training data generation for medical image segmentation

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-4/Code2/tree/main/DecoMR

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

https://github.com/2023-MindSpore-4/Code7/tree/main/SPPNet

SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2_FPNlite

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-4/Code8/tree/main/Auto-DeepLab

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

https://github.com/2023-MindSpore-4/Code8/tree/main/augvit

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

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