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/cgsaxner/UB_Segmentation

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

https://github.com/chakerouari/UNET_segmetation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chandan5362/Indian-Number-Plate-Recognition

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

https://github.com/chandrikadeb7/face-mask-detection

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/chaoyivision/SGGpoint

Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis

https://github.com/charlesYangM/py-faster-rcnn-80.28

Mask R-CNN

https://github.com/charlesshang/fastmaskrcnn

Mask R-CNN

https://github.com/charlychiu/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chauthehan/CRNN_OCR_CMND

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

https://github.com/cheng-01037/ssl_few_shot_med_img_seg

Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation

https://github.com/chenghan111/dnc

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/chengpengchen/repghost

MobileOne: An Improved One millisecond Mobile Backbone

https://github.com/chenmengyang/rename_later

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/chenwuperth/ClaRAN

Mask R-CNN

https://github.com/chenxi116/DeepLabv3.pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/chenyizi086/wu.2023.sigspatial

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chibohe/CdistNet-pytorch

CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

https://github.com/chihyanghsu0805/object_detection_yolo

Mask R-CNN

https://github.com/chinakook/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/chingswy/HumanPoseMemo

Learning from Synthetic Humans

https://github.com/chris-boson/fashion_mnist

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/chuanqichen/deepcoaching

Mask R-CNN

https://github.com/chufengt/ViRReq

Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding

https://github.com/chufengt/Visual-Recognition-by-Request

Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding

https://github.com/chuhan89/wsss-tissue

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

https://github.com/chunbolang/DCP

One-Shot Learning for Semantic Segmentation

https://github.com/chwilms/sos

Segment Anything

https://github.com/cindyxinyiwang/TextStyleTransfer

A Probabilistic Formulation of Unsupervised Text Style Transfer

https://github.com/cioppaanthony/rt-sbs

Pyramid Scene Parsing Network

https://github.com/cipri-tom/type-aware-crnn

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

https://github.com/cj-mclaughlin/segmentation_research

Pyramid Scene Parsing Network

https://github.com/cjxxx0/license

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

https://github.com/clairehester/face-mask-detector

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/clarencenhuang/dl-colorize

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/clemkoa/u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/clock-me/text-restyle

Multiple-Attribute Text Style Transfer

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