Papers with Code 아카이브

Segmentation

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코드

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

https://github.com/EliasKassapis/CARSSS

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

https://github.com/ElliotY-ML/Hippocampus_Segmentation_MRI

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

https://github.com/Elman295/Crowd_Counting_Density_Estimation

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

https://github.com/EmGarr/kerod

Mask R-CNN

https://github.com/Entodi/meshnet-pytorch

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/EquipoVandV/mmactionVandV

Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

https://github.com/EricKani/Segmenter-Based-on-OpenMMLab

Segmenter: Transformer for Semantic Segmentation

https://github.com/ErikGDev/instance-segmentation

Mask R-CNN

https://github.com/EvaBr/AnatomyNets

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/EvgenyDyshlyuk/Oil_Seep_Detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Evvvvvvvva/AutonomousDriving

Road Segmentation Using CNN and Distributed LSTM

https://github.com/Exclusivexc/CLISC

CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation

https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SWAG

Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning

https://github.com/FLoosli/CP_GAN

Learning to Segment via Cut-and-Paste

https://github.com/FNNDSC/pl-surfaces-fetus

Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels

https://github.com/Fabienne703/Data

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

https://github.com/FaceOnLive/Realtime-Background-Changer-SDK-Android

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Fangrn/caffe-segnet

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

https://github.com/FarihaHossain/SwinFSR

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

https://github.com/Fei-dong/SOLO_SORT

SOLO: Segmenting Objects by Locations

https://github.com/FelixGruen/tensorflow-u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/FenHua/Robust_Logo_Detection

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

https://github.com/FilippoAleotti/Dwarf-Tensorflow

Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation

https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation

Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification

https://github.com/FrancisCrickInstitute/Etch-a-Cell-Nuclear-Envelope

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/FrexG/ycgcr_leaf_segmentation

A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust

https://github.com/Futurne/vicreg-loss

VICRegL: Self-Supervised Learning of Local Visual Features

https://github.com/G0rav/Automatic_Background_Removal

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/GAOwy123/py-faster-rcnn

Mask R-CNN

https://github.com/GCVCG/VolETA-MetaFood

XMem++: Production-level Video Segmentation From Few Annotated Frames

https://github.com/GH3927/Mask-RCNN-applied-to-cranes

Mask R-CNN

https://github.com/GLSS-UDA/GLSS-UDA.github.io

Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search

https://github.com/GT-RIPL/L2C

Learning to Cluster for Proposal-Free Instance Segmentation

https://github.com/Gaoyiminggithub/Graphonomy-Panoptic

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

https://github.com/GatorSense/MILMinirhizotronSegmentation

Root Identification in Minirhizotron Imagery with Multiple Instance Learning

https://github.com/GeoDQ/Seismic-image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

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