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/fenglian425/Agriculture_AI

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/fepegar/highresnet

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

https://github.com/fepegar/resseg

Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning

https://github.com/fepegar/unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/fgnt/mms_msg

Deep clustering: Discriminative embeddings for segmentation and separation

https://github.com/filippogiruzzi/semantic_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/fiveai/saod

One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks

https://github.com/fkluger/tchl

Temporally Consistent Horizon Lines

https://github.com/flyreelai/sslayout360

SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama

https://github.com/fmahoudeau/ShelfNet-Human-Pose-Estimation

ShelfNet for Fast Semantic Segmentation

https://github.com/fmahoudeau/fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/foamliu/CRNN

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

https://github.com/foolwood/SiamMask

SiamMask: A Framework for Fast Online Object Tracking and Segmentation

https://github.com/fourmi1995/IronSegExperiment-LinkNet

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

https://github.com/fourmi1995/IronSegExperiment-PSPNet

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/fqnchina/CEILNet

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

https://github.com/frankkramer-lab/MIScnn/blob/master/miscnn/neural_network/metrics.py

Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

https://github.com/fregu856/deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/frgfm/Holocron

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/fsafe/Capstone

Mask R-CNN

https://github.com/fudan-zvg/DGMN2_MindSpore_Ascend

Dynamic Graph Message Passing Networks for Visual Recognition

https://github.com/funkdub/DMGN

Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images

https://github.com/funkey/mala

Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction

https://github.com/fuzhenxin/text_style_transfer

Style Transfer in Text: Exploration and Evaluation

https://github.com/fzi-forschungszentrum-informatik/mrf

SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

https://github.com/g-u-n/pycil

Learning without Forgetting

https://github.com/gaelmoccand/RoadSegmentation_CNN

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/gail-yxie/adawac

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

https://github.com/gallegi/T4E_MICCAI_BrainTumor

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

https://github.com/galprz/brain-tumor-segemntation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/galprz/brain-tumor-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ganeshpawar95/removebg1

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ganwanshui/simpleoccupancy

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

https://github.com/geekswaroop/Human-Parsing

Pyramid Scene Parsing Network

https://github.com/geekyutao/inpaint-anything

Segment Anything

https://github.com/geodekid/FCN

Fully Convolutional Networks for Semantic Segmentation

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