Papers with Code 아카이브

2D Semantic Segmentation

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전체 기법전체 데이터셋

하위 태스크

17

평가표

11

보존된 평가 기록

42

코드

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

https://github.com/martinferianc/ComBiNet

공식 코드

ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

https://github.com/marvinyu1995/hyctas

공식 코드

Real-Time Image Segmentation via Hybrid Convolutional-Transformer Architecture Search

https://github.com/masih4/foot_ulcer_segmentation

공식 코드

Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks

https://github.com/masilab/3dux-net

공식 코드

3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

https://github.com/masilab/deform-uxnet

공식 코드

DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution

https://github.com/masilab/repux-net

공식 코드

Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation

https://github.com/masilab/unest

공식 코드

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

https://github.com/matheus-viana/heterogeneous_sampling

공식 코드

A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts

https://github.com/mathilde-b/SFDA

공식 코드

Source-Free Domain Adaptation for Image Segmentation

https://github.com/mathilde-b/SRDA

공식 코드

Source-Relaxed Domain Adaptation for Image Segmentation

https://github.com/mathilde-b/tta

공식 코드

Test-Time Adaptation with Shape Moments for Image Segmentation

https://github.com/mathlee/lasnet

공식 코드

RGB-T Semantic Segmentation with Location, Activation, and Sharpening

https://github.com/mathpluscode/imgx-diffseg

공식 코드

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

https://github.com/mathwrx/BA-Net/blob/main/encoding/models/BANet.py

공식 코드

Boundary-aware Context Neural Network for Medical Image Segmentation

https://github.com/mattdl/DUA

공식 코드

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

https://github.com/matteo-bastico/mi-seg

공식 코드

A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers

https://github.com/matthi99/deepgmm

공식 코드

Deep Gaussian mixture model for unsupervised image segmentation

https://github.com/mattpfr/lmanet

공식 코드

Local Memory Attention for Fast Video Semantic Segmentation

https://github.com/mattwallingford/neuralradiancefieldcodebooks

공식 코드

Neural Radiance Field Codebooks

https://github.com/maudzung/SFA3D

공식 코드

Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment

https://github.com/mawanqin2002/stacom2022ma

공식 코드

Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels

https://github.com/max810/XMem2

공식 코드

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

https://github.com/maxiao0234/ttga

공식 코드

Test-Time Generative Augmentation for Medical Image Segmentation

https://github.com/maxjiang93/DDSL

공식 코드

DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals

https://github.com/maxwell0027/lefed

공식 코드

Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

https://github.com/mazurowski-lab/SlicerSegmentHumanBody

공식 코드

Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

https://github.com/mazurowski-lab/finetune-sam

공식 코드

How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

https://github.com/mazurowski-lab/sam-texturalconfusion-metrics

공식 코드

Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects

https://github.com/mazurowski-lab/segment-anything-medical

공식 코드

Segment Anything Model for Medical Image Analysis: an Experimental Study

https://github.com/mazurowski-lab/segment-anything-medical-evaluation

공식 코드

Segment Anything Model for Medical Image Analysis: an Experimental Study

https://github.com/mazurowski-lab/single-image-test-time-adaptation

공식 코드

Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation

https://github.com/mazurowski-lab/slm-sam2

공식 코드

Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

https://github.com/mbkiss/2detectcodes

공식 코드

2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

https://github.com/mbzuai-oryx/videomolmo

공식 코드

VideoMolmo: Spatio-Temporal Grounding Meets Pointing

https://github.com/mc-lan/text4seg

공식 코드

Text4Seg: Reimagining Image Segmentation as Text Generation

https://github.com/mcahny/vps

공식 코드

Video Panoptic Segmentation

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