Papers with Code 아카이브

Segmentation

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평가표

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보존된 평가 기록

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코드

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

https://github.com/PaddleCV-SIG/EISeg

EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

https://github.com/PaddlePaddle/PASSL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/PaddlePaddle/Paddle3D

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

https://github.com/PaddlePaddle/PaddleDetection

Microsoft COCO: Common Objects in Context

https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

https://github.com/PaddlePaddle/PaddleOCR

Searching for MobileNetV3

https://github.com/PaddlePaddle/PaddleRS

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/segmentation/cfbi.md

Collaborative Video Object Segmentation by Foreground-Background Integration

https://github.com/PaddlePaddle/PaddleYOLO

Focal Modulation Networks

https://github.com/Pal-kid404/F-ANcGAN

F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of Nanoparticles

https://github.com/PatRyg99/HausdorffLoss

Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks

https://github.com/PatriciaRodrigues1994/Satellite-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/PatxiofromAlphensign/subword-nmt-mods

BPE-Dropout: Simple and Effective Subword Regularization

https://github.com/PaulSudarshan/Language-Classification-Using-Naive-Bayes-Algorithm

Neural Machine Translation of Rare Words with Subword Units

https://github.com/Paultool/segnet

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

https://github.com/PengyiZhang/MIADeepSSL

Constrained-CNN losses for weakly supervised segmentation

https://github.com/PhilChina/CA-Net

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

https://github.com/PkuRainBow/OCNet

Interlaced Sparse Self-Attention for Semantic Segmentation

https://github.com/PkuRainBow/OCNet.pytorch

Interlaced Sparse Self-Attention for Semantic Segmentation

https://github.com/Plrbear/HGR-Net

HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition

https://github.com/Plrbear/biomedical-image-segmentation

Multi-Level Contextual Network for Biomedical Image Segmentation

https://github.com/PlumedSerpent/tmp_perspective_map

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/PointCloudYC/SQN_tensorflow

One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation

https://github.com/PolinaDruzhinina/mobilenet

Searching for MobileNetV3

https://github.com/PolinaDruzhinina/modelnet

Searching for MobileNetV3

https://github.com/Popcorn-sugar/Deep_v2

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Praveen94/pytorch-encoding

Context Encoding for Semantic Segmentation

https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids

Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unetr_btcv_segmentation_3d.ipynb

UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/PuchatekwSzortach/voc_fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/PurabPatel555/Pytorch-UNet-Brain-MRI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Pxtri2156/AdelaiDet_v2

SOLO: Segmenting Objects by Locations

https://github.com/Qinhj07/ATOMCode

Mask R-CNN

https://github.com/Qsingle/imed_vision

SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

https://github.com/Qualcomm-AI-research/InverseForm

InverseForm: A Loss Function for Structured Boundary-Aware Segmentation

https://github.com/RPIDIAL/SAMix

DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets

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