Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/JeongHyunJin/Pix2PixHD

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/Jeongseungwoo/Fusion-net

FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

https://github.com/Jeongseungwoo/U_net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Jeongyun-Lee-0423/Classification

Deep Residual Learning for Image Recognition

https://github.com/JeremyBYU/FastGaussianAccumulator

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

https://github.com/JerryX1110/awesome-rvos

End-to-End Referring Video Object Segmentation with Multimodal Transformers

https://github.com/JesonZhang822/Dogs-VS-Cats-Project

Deep Residual Learning for Image Recognition

https://github.com/JetRunner/PABEE

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/JeyesHan/P2LR

Deep Residual Learning for Image Recognition

https://github.com/Jia-Research-Lab/GridMask

GridMask Data Augmentation

https://github.com/Jia-Research-Lab/MSAD

Slimmable Neural Networks

https://github.com/Jiang-Muyun/Open3D-Semantic-KITTI-vis

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

https://github.com/JiangWenPL/multiperson

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Jiaxin-Ye/DepMamba

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

https://github.com/JifeiWang-WHU/Pytorch_Building_extraction

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JinLi711/Attention-Augmented-Convolution

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/JinLi711/Convolution_Variants

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/JingZhang617/UCNet

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

https://github.com/JingfeiHuang/DGCNN-Paddle

Dynamic Graph CNN for Learning on Point Clouds

https://github.com/JingjunYi/SET

Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation

https://github.com/JinyongJeong/DeeplabV3_Apolloscape_and_CityScape

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/JinyuanLiu-CV/TarDAL

Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection

https://github.com/Jittor-Image-Models/Jittor-Image-Models

Visual Attention Network

https://github.com/Jittor/InstanceSegmentation-jittor

Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/Jittor/JSeg

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

https://github.com/Jo-dsa/SemanticSeg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JoeTracey/Portfolio

Deep Residual Learning for Image Recognition

https://github.com/JoegameZhou/Segmenter_workers

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/JoelPendleton/Edge-Detection-CNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JoelPendleton/Object-Contour-Detection-CNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Joesher15/deep_learning_project

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

https://github.com/JohnRomanelis/KPConv_torch_geometric

KPConv: Flexible and Deformable Convolution for Point Clouds

https://github.com/JohnnyHopp/Panoptic-DeepLab-Mobilenetv2

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

https://github.com/JohnsonSign/PointCMP

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/JonasFrey96/ASL

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/JonasFrey96/ml-hypersim-pytorch

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

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