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/manthanpatel2/Object-Detection-on-a-Frame

Microsoft COCO: Common Objects in Context

https://github.com/manuelhz/dissertation

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

https://github.com/marc-gorriz/CEAL-Medical-Image-Segmentation

Cost-Effective Active Learning for Melanoma Segmentation

https://github.com/marccoru/marinedebrisdetector

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

https://github.com/marcdcfischer/punetr

Prompt Tuning for Parameter-efficient Medical Image Segmentation

https://github.com/marcoancona/TorchPruner

Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning

https://github.com/marine-debris/marine-debris.github.io

https://github.com/mariob6/style_text

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/mariosfourn/ScienceBowl

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/marload/ConvNets-TensorFlow2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/marrlab/dispr

Diffusion Models for Implicit Image Segmentation Ensembles

https://github.com/martiansideofthemoon/style-transfer-paraphrase

Multiple-Attribute Text Style Transfer

https://github.com/martinsbruveris/tensorflow-image-models

Segment Anything

https://github.com/marvinchung/hw5-textstyletransfer

Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation

https://github.com/massucattoj/unet-model

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/matanrein/Understanding-Clouds-from-Satellite-Images

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mateuszbuda/brain-segmentation-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mathildor/DeepLab-v3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/mattangus/fast-semantic-segmentation

Pyramid Scene Parsing Network

https://github.com/matteo-bastico/coupledlaplacian

A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers

https://github.com/mattgorb/cnn_bold5000

BOLD5000: A public fMRI dataset of 5000 images

https://github.com/matthieuced/gscnn-apply-to-airborne-magnetic

Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

https://github.com/mattmacy/vnet.pytorch

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

https://github.com/maxfrei750/DeepParticleNet

Mask R-CNN

https://github.com/maxfrei750/FibeR-CNN

Mask R-CNN

https://github.com/mayu-ot/rethinking-evs

Rethinking the Evaluation of Video Summaries

https://github.com/mberkay0/pretrained-backbones-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mdt48/semantic-segmentation-pytorch

Semantic Understanding of Scenes through the ADE20K Dataset

https://github.com/mducducd/Shadow-Removal

Single Image Reflection Separation with Perceptual Losses

https://github.com/mducducd/ghost-free-shadow-removal

Single Image Reflection Separation with Perceptual Losses

https://github.com/medicinetoken/medical-graph-rag

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

https://github.com/medicl-vu/oct-mosaicking

OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study

https://github.com/mediteamC/teamC

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

https://github.com/megvii-research/basecls/tree/main/zoo/public/mbnet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/mehul-k5/Double-Unet

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

https://github.com/meijieru/crnn.pytorch

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

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