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/DecentMakeover/mdetect

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Deci-AI/super-gradients

Deep Residual Learning for Image Recognition

https://github.com/Deeksha-5/STAN_Pytorch_Implementation

Stan: Small tumor-aware network for breast ultrasound image segmentation

https://github.com/DeepK/inpainting

Region-of-interest guided Supervoxel Inpainting for Self-supervision

https://github.com/DeepMark/deepmark

Deep Residual Learning for Image Recognition

https://github.com/DeepMicroscopy/nnUNet

Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?

https://github.com/DeepMotionAIResearch/DenseASPP

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

https://github.com/DeepSceneSeg/SSMA

Self-Supervised Model Adaptation for Multimodal Semantic Segmentation

https://github.com/DeepTrial/Retina-VesselNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DeepVoltaire/Fast-SCNN

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/DengPingFan/Inf-Net

D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism

https://github.com/DengpanFu/LUPerson

Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks

https://github.com/DerrickGXD/unsupervised-keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DetectionTeamUCAS/FCOS_GluonCV

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/DetectionTeamUCAS/FPN_Tensorflow

Feature Pyramid Networks for Object Detection

https://github.com/DevBruce/CutMixImageDataGenerator_For_Keras

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

https://github.com/DevBruce/PyTorch-ISBI2012-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DevBruce/Segmentation-UNet-PyTorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/DevPranjal/reproduction-review-kd

Distilling Knowledge via Knowledge Review

https://github.com/Di-Is/stylegan2-ada-pytorch

Efficient Attention: Attention with Linear Complexities

https://github.com/DiMarzioRock7/Aerial-Semantic-Segmentation-Drone

Pyramid Scene Parsing Network

https://github.com/DiMarzioRock7/PSPNet

Pyramid Scene Parsing Network

https://github.com/Diksha942/Visual-Transformers

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

https://github.com/DingXiaoH/GSM-SGD

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/DingXiaoH/ResRep

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/Dir-b/PointNet

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

https://github.com/DivJAth/DeepLearning5922

Mask R-CNN

https://github.com/DivaniMandi/myCustomDataset_yolact

YOLACT: Real-time Instance Segmentation

https://github.com/Dmitrsl/Tools

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/DoTung-bkhn/multiclass-segmentation

Pyramid Scene Parsing Network

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/DominickZhang/Distillation-Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/DominikBatic/EndoViT

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

https://github.com/DongjuSin/computer-vision-proj

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

https://github.com/DoranLyong/SPANet-official

Spectral-Adaptive Modulation Networks for Visual Perception

https://github.com/DrBoltzmann/CarND-Semantic-Segmentation

Fully Convolutional Networks for Semantic Segmentation

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