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/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sunhui1234/haha

Mask R-CNN

https://github.com/sunnyahlawat1713/face-mask-detector-python

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/sunqiangxtcsun/faster-rcnn

Mask R-CNN

https://github.com/sunset1995/pytorch-layoutnet

LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

https://github.com/sunshenggu/xc_eval_pcdet

From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

https://github.com/sunye23/unseg

UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation

https://github.com/supermedintel/medical-sam-adapter

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

https://github.com/supermedintel/medical-sam2

Medical SAM 2: Segment medical images as video via Segment Anything Model 2

https://github.com/supermedintel/one-prompt

One-Prompt to Segment All Medical Images

https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

https://github.com/susomena/DeepSlowMotion

Deep Video Deblurring

https://github.com/switch23/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/switchablenorms/SwitchNorm_Segmentation

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

https://github.com/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/syscv/sam-hq

Segment Anything

https://github.com/szubing/uniood

VisDA: The Visual Domain Adaptation Challenge

https://github.com/taconite/DDD-Differentiable-Dual-Decomposition

End-to-end Training of CNN-CRF via Differentiable Dual-Decomposition

https://github.com/taha7ussein007/Papers_Implementation/tree/main/Paper_Implementation_From_Scratch/UNet_FromScratch_Pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/tahaemara/LiteSeg

LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation

https://github.com/taigw/geodesic_distance

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

https://github.com/talshaharabany/DeepACM2D

End to End Trainable Active Contours via Differentiable Rendering

https://github.com/tamasino52/UNETR

UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/tamasino52/UNeTR-Pose

UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/tamerthamoqa/3D-mri-brain-tumour-image-segmentation-medical-decathlon-tensorflow

The Medical Segmentation Decathlon

https://github.com/tamirshor7/latents-guided-thermography

CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

https://github.com/tamnil/esp32-xtensa-study

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/tangjiapeng/diffuscene

Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

https://github.com/tanmaysheoran/CRNN-Sequence-Text-Recognition

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

https://github.com/tantara/JejuNet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/tanyanair/segmentation_uncertainty

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/taojlu/Liver-and-Tumor-Segmentation-Papers-List

Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study

https://github.com/taoroalin/WNet

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

https://github.com/tarit21/Speech-Enhancement-module

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/tarolangner/mri-biometry

Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images

https://github.com/tarolangner/ukb_segmentation

TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

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