Papers with Code 아카이브

Segmentation

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하위 태스크

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평가표

5

보존된 평가 기록

6

코드

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

https://github.com/MindSpore-paper-code-2/code2/tree/main/REDNet30

RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

https://github.com/MindSpore-paper-code-2/code2/tree/main/RefineNet

Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations

https://github.com/MindSpore-paper-code-2/code2/tree/main/nnUNet

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

https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer

Point Transformer

https://github.com/MindSpore-paper-code-2/code3/tree/main/SSIM-AE

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

https://github.com/MindSpore-paper-code-2/code3/tree/main/TCN

MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation

https://github.com/MindSpore-paper-code-2/code3/tree/main/UNet3%2B

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

https://github.com/MindSpore-paper-code-2/code3/tree/main/Unet3d

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

https://github.com/MindSpore-paper-code-2/code3/tree/main/Yolact%2B%2B

YOLACT++: Better Real-time Instance Segmentation

https://github.com/MindSpore-paper-code-2/code3/tree/main/tinydarknet

Mask R-CNN

https://github.com/MindSpore-paper-code-3/code1/tree/main/Auto-DeepLab

FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

https://github.com/MindSpore-paper-code-3/code10/tree/main/FCN8s

Fully Convolutional Networks for Semantic Segmentation

https://github.com/MindSpore-paper-code-3/code10/tree/main/fastscnn

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/MindSpore-paper-code-3/code3/tree/main/FCN8s

Fully Convolutional Networks for Semantic Segmentation

https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceNet

Fully Convolutional Networks for Semantic Segmentation

https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_dcn

Mask R-CNN

https://github.com/MindSpore-paper-code-3/code3/tree/main/fastscnn

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/MindSpore-paper-code-3/code5/tree/main/REDNet30

RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

https://github.com/MindSpore-paper-code-3/code6/tree/main/Auto-DeepLab

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

https://github.com/MindSpore-paper-code-3/code6/tree/main/convnext

Global Context Vision Transformers

https://github.com/MindSpore-paper-code-3/code7/tree/main/FCN8s

DSNet: Automatic Dermoscopic Skin Lesion Segmentation

https://github.com/MindSpore-paper-code-3/code7/tree/main/essay-recogination

OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold

https://github.com/MindSpore-paper-code-3/code7/tree/main/fastscnn

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/MindSpore-paper-code-3/code8/tree/main/essay-recogination

OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold

https://github.com/MindSpore-paper-code-3/code8/tree/main/fastscnn

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/MindSpore-paper-code-3/code9/tree/main/DeepLabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN

Mask R-CNN

https://github.com/MindSpore-paper-code-3/code9/tree/main/OCRNet

Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation

https://github.com/MindSpore-paper-code-3/code9/tree/main/ResNet

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/MindSpore-scientific-2/code-10/tree/main/Uncertainty_Calibration_Object_Detection

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

https://github.com/MindSpore-scientific-2/code-5/tree/main/D-Unet_Mindspore

UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

https://github.com/MindSpore-scientific-2/code-9/tree/main/D-Unet_Mindspore

D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation

https://github.com/MindSpore-scientific/code-10/tree/main/MicAugment

Dilated Neighborhood Attention Transformer

https://github.com/MindSpore-scientific/code-10/tree/main/Uncertainty_Calibration_Object_Detection

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

https://github.com/MindSpore-scientific/code-10/tree/main/rand_conv-master

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

https://github.com/MindSpore-scientific/code-12/tree/main/D-Unet_Mindspore

D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation

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