Papers with Code 아카이브

Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

5

보존된 평가 기록

6

코드

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

https://github.com/hzlbbfrog/bgcrack

공식 코드

Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset

https://github.com/hzlbbfrog/crackmamba

공식 코드

Mamba meets crack segmentation

https://github.com/hzykent/JSENet

공식 코드

JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds

https://github.com/i-sahajmistry/nnunet_brats2023

공식 코드

Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentation

https://github.com/i-vesseg/multivesseg

공식 코드

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

https://github.com/iMED-Lab/CS-Net

공식 코드

CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

https://github.com/iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network

공식 코드

Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

https://github.com/iWeisskohl/Evidential-neural-network-for-lymphoma-segmentation

공식 코드

Evidential segmentation of 3D PET/CT images

https://github.com/iamshubhamgupto/bandnet

공식 코드

Analysis and application of multispectral data for water segmentation using machine learning

https://github.com/iantsen/hecktor

공식 코드

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

https://github.com/ibrain-lab/pingu

공식 코드

Perivascular space Identification Nnunet for Generalised Usage (PINGU)

https://github.com/icans-strasbourg/psat

공식 코드

PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning

https://github.com/icputrd/SLRNet

공식 코드

SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images

https://github.com/icsc-spoke2-repo/fastextendedvision-deepcluster

공식 코드

Datacube segmentation via Deep Spectral Clustering

https://github.com/ictnlp/diseg

공식 코드

End-to-End Simultaneous Speech Translation with Differentiable Segmentation

https://github.com/idea-research/maskdino

공식 코드

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/idea-research/mp-former

공식 코드

MP-Former: Mask-Piloted Transformer for Image Segmentation

https://github.com/idea-research/openseed

공식 코드

A Simple Framework for Open-Vocabulary Segmentation and Detection

https://github.com/idealwei/ASANet

공식 코드

Affinity Space Adaptation for Semantic Segmentation Across Domains

https://github.com/idu-cvlab/cov19d_3rd

공식 코드

COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline

https://github.com/ifnspaml/CIL_Segmentation

공식 코드

Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels

https://github.com/ifnspaml/UBNA

공식 코드

Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations

https://github.com/ifnspaml/amodalcityscapes

공식 코드

Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline

https://github.com/ifnspaml/revt

공식 코드

A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation

https://github.com/ifnspaml/s-amodal

공식 코드

Foundation Models for Amodal Video Instance Segmentation in Automated Driving

https://github.com/ihdia/Palmira

공식 코드

Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts

https://github.com/iigroup/hvisnet

공식 코드

Real-time Human-Centric Segmentation for Complex Video Scenes

https://github.com/iipl-hangzhoudianziuniversity/defn-pytorch

공식 코드

DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation

https://github.com/iiscleap/DIHARD_2019_baseline_alltracks

공식 코드

The Second DIHARD Diarization Challenge: Dataset, task, and baselines

https://github.com/ilareina/fullyattentional

공식 코드

Fully Attentional Network for Semantic Segmentation

https://github.com/imagingyeditepe/segmentation-of-teeth-in-panoramic-x-ray-image

공식 코드

Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

https://github.com/imatge-upc/liverseg-2017-nipsws

공식 코드

Detection-aided liver lesion segmentation using deep learning

https://github.com/imatge-upc/medical-2017-nipsw

공식 코드

Cost-Effective Active Learning for Melanoma Segmentation

https://github.com/imatge-upc/mri-braintumor-segmentation

공식 코드

MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

https://github.com/imatge-upc/rsis

공식 코드

Recurrent Neural Networks for Semantic Instance Segmentation

https://github.com/imatge-upc/rvos

공식 코드

RVOS: End-to-End Recurrent Network for Video Object Segmentation

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