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/stegmaierj/CellShapeAnalysis

공식 코드

Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples

https://github.com/stegmaierj/Cellpose3D

공식 코드

Robust 3D Cell Segmentation: Extending the View of Cellpose

https://github.com/stegmaierj/diffusionmodelsforimagesynthesis

공식 코드

Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets

https://github.com/stegmuel/croc

공식 코드

CrOC: Cross-View Online Clustering for Dense Visual Representation Learning

https://github.com/steve-zeyu-zhang/SegReg

공식 코드

SegReg: Segmenting OARs by Registering MR Images and CT Annotations

https://github.com/stevezs315/attukan

공식 코드

Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation

https://github.com/strivezs/alps

공식 코드

ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model

https://github.com/stuliu/dfc2025track1

공식 코드

Learning from Noisy Pseudo-labels for All-Weather Land Cover Mapping

https://github.com/su-hui-zz/sasformer

공식 코드

SASFormer: Transformers for Sparsely Annotated Semantic Segmentation

https://github.com/subtri/video_inference

공식 코드

Semantic Video Segmentation : Exploring Inference Efficiency

https://github.com/sud0301/semisup-semseg

공식 코드

Semi-Supervised Semantic Segmentation with High- and Low-level Consistency

https://github.com/sudraj2002/fsgrasp

공식 코드

Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning

https://github.com/sugar0725/seg-wild

공식 코드

Seg-Wild: Interactive Segmentation based on 3D Gaussian Splatting for Unconstrained Image Collections

https://github.com/suhas-srinath/genselfdiff-his

공식 코드

GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation

https://github.com/suhlemeyer/novelty-learning

공식 코드

Towards Unsupervised Open World Semantic Segmentation

https://github.com/suhwan-cho/FindTrack

공식 코드

Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation

https://github.com/sumedhvdatar/PortraitSegmentation

공식 코드

Portrait Segmentation Using Deep Learning

https://github.com/sun-1024/da-transunet

공식 코드

DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation

https://github.com/sunalbert/Sequential-patch-based-segmentation

공식 코드

Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning

https://github.com/sunfan-bvb/boundarydouloss

공식 코드

Boundary Difference Over Union Loss For Medical Image Segmentation

https://github.com/sunjesse/shape-attentive-unet

공식 코드

SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

https://github.com/sunjiahao1999/spformer

공식 코드

Superpoint Transformer for 3D Scene Instance Segmentation

https://github.com/sunny599/aaai24-3dwssg-mma

공식 코드

Multi-modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation

https://github.com/sunshuofeng/X-3D

공식 코드

X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition

https://github.com/super-tadory/primgeoseg

공식 코드

Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation

https://github.com/superz-liu/polarbev

공식 코드

Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation

https://github.com/supreethmv/q-seg

공식 코드

Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation

https://github.com/susaha/ctrl-uda

공식 코드

Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation

https://github.com/susaha/edaps

공식 코드

EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation

https://github.com/suuujm/spss

공식 코드

Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation

https://github.com/suyanfei/DLA-Net

공식 코드

DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds

https://github.com/sveinnpalsson/semivaebrats

공식 코드

Semi-Supervised Variational Autoencoder for Survival Prediction

https://github.com/svrtk/masc-multi-task-segmentation-and-classification

공식 코드

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

https://github.com/syanng/blazeneo

공식 코드

BlazeNeo: Blazing fast polyp segmentation and neoplasm detection

https://github.com/syau-miao/seg4maize

공식 코드

Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud

https://github.com/sybeam27/IAP-ZSAS

공식 코드

Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation

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