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/yangyucheng000/MSpaper/tree/main/UNet3%2B

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

https://github.com/yangyucheng000/Mask-RCNN

Mask R-CNN

https://github.com/yangyucheng000/PSPNet

Pyramid Scene Parsing Network

https://github.com/yangyucheng000/Paper-4/tree/main/TAA_MindSpore

TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation

https://github.com/yangyucheng000/Papers/tree/main/STAN

Stan: Small tumor-aware network for breast ultrasound image segmentation

https://github.com/yangyucheng000/University/tree/main/model-1/oneformer

OneFormer: One Transformer to Rule Universal Image Segmentation

https://github.com/yangyucheng000/University/tree/main/model-2/groupvit

GroupViT: Semantic Segmentation Emerges from Text Supervision

https://github.com/yangyucheng000/University/tree/main/model-3/sam

Underwater Camouflaged Object Tracking Meets Vision-Language SAM2

https://github.com/yangyucheng000/University/tree/main/model-3/seggpt

SegGPT: Segmenting Everything In Context

https://github.com/yangyucheng000/maskrcnn_mobilenetv1

Mask R-CNN

https://github.com/yangyucheng000/mobilenet_v2/blob/main/mobilenet_v2.py

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/yaoyao-liu/mnemonics

Learning without Forgetting

https://github.com/yaozhang93/mmformer

Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation

https://github.com/yaq007/Autofocus-Layer

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

https://github.com/yashdani/Object-Detection-Using-YOLOv3-Algorithm

Microsoft COCO: Common Objects in Context

https://github.com/yashs662/LMBiS-Net

LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation

https://github.com/yassineAlouini/airbus_ship_detection

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

https://github.com/yassineAlouini/data-science-bowl-2018

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/yassouali/pytorch_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ycszen/ContextPrior

Context Prior for Scene Segmentation

https://github.com/yczhang1017/SSD_resnet_pytorch

Mask R-CNN

https://github.com/yelantingfeng/pyLucid

Lucid Data Dreaming for Video Object Segmentation

https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

https://github.com/yformer/EfficientSAM

Efficient Track Anything

https://github.com/yihanhu-2022/diffmatte

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers

https://github.com/yinanzhu12/SegNet-keras

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

https://github.com/yinanzhu12/SegNet-keras-implementation

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

https://github.com/yingernnn/transclawunet

TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation

https://github.com/yingkaisha/keras-unet-collection

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

https://github.com/yiskw713/boundary_loss_for_remote_sensing

Boundary Loss for Remote Sensing Imagery Semantic Segmentation

https://github.com/yj-littlesky/py-faster-rcnn

Mask R-CNN

https://github.com/yjt2018/awesome-remote-sensing-change-detection

xBD: A Dataset for Assessing Building Damage from Satellite Imagery

https://github.com/ykasten/layered-neural-atlases

Mask R-CNN

https://github.com/yoonkim/neural-qcfg

StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

https://github.com/yossigandelsman/DoubleDIP

"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors

https://github.com/ytx21cn/CDCL-human-seg

Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation

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