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/waspinator/deep-learning-explorer

Fully Convolutional Networks for Semantic Segmentation

https://github.com/wau/Unsupervised-SIS

W-Net: A Deep Model for Fully Unsupervised Image Segmentation

https://github.com/weinman/cnn_lstm_ctc_ocr

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

https://github.com/weitunglin/pixmamba

VM-UNet: Vision Mamba UNet for Medical Image Segmentation

https://github.com/weiwang2330/siamese

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

https://github.com/wenhui0206/Robust-TransBTS

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

https://github.com/west-gates/3DCNN-Vis

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

https://github.com/westgate458/xb-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/wgoldie/canvasdrawer

Unsupervised Image to Sequence Translation with Canvas-Drawer Networks

https://github.com/whieya/learning-to-compose

Object-Centric Slot Diffusion

https://github.com/willyfh/farl-face-segmentation

General Facial Representation Learning in a Visual-Linguistic Manner

https://github.com/wind754203900/SCNN-TF2

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

https://github.com/winstonhutiger/phiseg-code

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

https://github.com/witcher425/CHINESEOCR

COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

https://github.com/wjcheon/MedicalImageSegmentation_Pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/wkentaro/pytorch-fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/wlstyql/keras_with_mat_for_unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/woaixuexixuexi/PSANet

One-Shot Learning for Semantic Segmentation

https://github.com/woans0104/project_review

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

https://github.com/woans0104/sk_project

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

https://github.com/woonhahaha/place-classification

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

https://github.com/work-mohit/UNet-from-scratch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/woshiyyya/Auto-Retoucher-pytorch

Auto-Retoucher(ART) - A framework for Background Replacement and Image Editing

https://github.com/wri/restoration-mapper

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

https://github.com/wri/sentinel-tree-cover

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

https://github.com/wudao001/LSMVOS

LSMVOS: Long-Short-Term Similarity Matching for Video Object

https://github.com/xTRam1/Object-Detection-on-Custom-Dataset

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/xahidbuffon/SUIM

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/xahidbuffon/SVAM-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/xiangyu8/RGANet_evaluation

Realtime Global Attention Network for Semantic Segmentation

https://github.com/xiaochus/MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/xiaofeng-han-res/mf-rv

Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision

https://github.com/xiaofengShi/CHINESE-OCR

COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

https://github.com/xiaohangzhan/deocclusion

Self-Supervised Scene De-occlusion

https://github.com/xiaolonw/TimeCycle

Learning Correspondence from the Cycle-Consistency of Time

https://github.com/xiaoqi-zhao-dlut/msnet-m2snet

M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

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