Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/2023-MindSpore-1/ms-code-25

https://github.com/2023-MindSpore-1/ms-code-34

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/2023-MindSpore-1/ms-code-35

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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-48

Point Transformer

https://github.com/2023-MindSpore-1/ms-code-53

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/Pix2PixHD1

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

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-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-69

End-to-End Object Detection with Transformers

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

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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

Multimodal Token Fusion for Vision Transformers

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/Unet3d

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

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

YOLACT++: Better Real-time Instance Segmentation

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

YOLACT++: Better Real-time Instance 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/vit_base

DeiT III: Revenge of the ViT

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-1/ms-code-7/tree/main/wave_mlp

An Image Patch is a Wave: Phase-Aware Vision MLP

https://github.com/2023-MindSpore-1/ms-code-8/tree/main/PointNet2

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/2023-MindSpore-4/Code-5/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/2023-MindSpore-4/Code-5/tree/main/HRNetW48_seg

U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Prediction

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

Hire-MLP: Vision MLP via Hierarchical Rearrangement

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

Index Network

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

HarDNet: A Low Memory Traffic Network

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

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

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

Lite-HRNet: A Lightweight High-Resolution Network

https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code-5/tree/main/mobilenetV3_small_x1_0

Searching for MobileNetV3

https://github.com/2023-MindSpore-4/Code-5/tree/main/mobilenetv3_large

Searching for MobileNetV3

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

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