Papers with Code 아카이브

Semantic Segmentation

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전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

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

https://github.com/LayneH/LEWEL

공식 코드

Learning Where to Learn in Cross-View Self-Supervised Learning

https://github.com/LeeBY68/Hier-SLAM

공식 코드

Hier-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian Splatting

https://github.com/Leedeng/Line-Counter

공식 코드

LineCounter: Learning Handwritten Text Line Segmentation by Counting

https://github.com/Levishery/Flywire-Neuron-Tracing

공식 코드

Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

https://github.com/LiBingyu01/FGA-seg

공식 코드

FGAseg: Fine-Grained Pixel-Text Alignment for Open-Vocabulary Semantic Segmentation

https://github.com/LiJiahang617/HAPNet

공식 코드

HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion

https://github.com/LiYingwei/Regional-Homogeneity

공식 코드

Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses

https://github.com/LifeBeyondExpectations/PointMixer

공식 코드

PointMixer: MLP-Mixer for Point Cloud Understanding

https://github.com/LiheYoung/Depth-Anything

공식 코드

Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

https://github.com/LiheYoung/FreeMask

공식 코드

FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

https://github.com/LiheYoung/ST-PlusPlus

공식 코드

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

https://github.com/LiheYoung/UniMatch

공식 코드

Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

https://github.com/LiheYoung/UniMatch-V2

공식 코드

UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation

https://github.com/Limingxing00/MitoEM2021-Challenge

공식 코드

Advanced Deep Networks for 3D Mitochondria Instance Segmentation

https://github.com/LinZhuoChen/SGNet

공식 코드

Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation

https://github.com/LingyiHongfd/LVOS

공식 코드

LVOS: A Benchmark for Long-term Video Object Segmentation

https://github.com/Linxius/UrbanScene3D

공식 코드

Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset

https://github.com/LittleWat/multichannel-semseg-with-uda

공식 코드

Multichannel Semantic Segmentation with Unsupervised Domain Adaptation

https://github.com/LiuTingWed/CriDiff

공식 코드

CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation

https://github.com/Lixy1997/Group-WSSS

공식 코드

Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

https://github.com/Lonepic/SPIB

공식 코드

Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining

https://github.com/LongchaoDa/SegmentAsYouWish

공식 코드

Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images

https://github.com/LouisYuxuLu/ERANet

공식 코드

Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions

https://github.com/Luc4Gui/CAVM

공식 코드

CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis

https://github.com/LucasFidon/GeneralizedWassersteinDiceLoss

공식 코드

Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge

https://github.com/LucasFidon/HardnessWeightedSampler

공식 코드

Distributionally Robust Deep Learning using Hardness Weighted Sampling

https://github.com/Luffy03/DCA

공식 코드

Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation

https://github.com/Luodian/MADAN

공식 코드

Multi-source Domain Adaptation for Semantic Segmentation

https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning

공식 코드

Polygonal Building Segmentation by Frame Field Learning

https://github.com/MASILab/SynSeg-Net

공식 코드

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

https://github.com/MCG-NKU/SERE

공식 코드

SERE: Exploring Feature Self-relation for Self-supervised Transformer

https://github.com/MDL-UzL/DenseSeg

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DenseSeg: Joint Learning for Semantic Segmentation and Landmark Detection Using Dense Image-to-Shape Representation

https://github.com/MECLabTUDA/ACS

공식 코드

Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation

https://github.com/MECLabTUDA/Lifelong-nnUNet

공식 코드

Continual Hippocampus Segmentation with Transformers

https://github.com/MECLabTUDA/M3D-NCA

공식 코드

M3D-NCA: Robust 3D Segmentation with Built-in Quality Control

https://github.com/MIC-DKFZ/MedNeXt

공식 코드

MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

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