Papers with Code 아카이브

Semantic Segmentation

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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/PramaLLC/BEN

공식 코드

BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation

https://github.com/PratikGarai/Semantic-Segmentation-UNet-Federated

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FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

https://github.com/Precision-Medical-Imaging-Group/BraTS2023-inferCode

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Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging

https://github.com/Precision-Medical-Imaging-Group/BraTS2024-PEDS

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Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

https://github.com/Prof-Pratik-Shah-Lab-UCI/skin_project_2024

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Uncertainty Quantified Deep Learning and Regression Analysis Framework for Image Segmentation of Skin Cancer Lesions

https://github.com/Project-MONAI/MONAI

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Automated head and neck tumor segmentation from 3D PET/CT

https://github.com/Project-MONAI/VISTA

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VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21

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

https://github.com/Project-MONAI/research-contributions/tree/master/UNETR/BTCV

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UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/Project-MONAI/tutorials/tree/master/automl/DiNTS

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DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

https://github.com/Purushothaman-natarajan/VALE-Explainer

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VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language Models

https://github.com/PythonOT/POT/blob/master/ot/da.py

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Optimal Transport for Multi-source Domain Adaptation under Target Shift

https://github.com/QIU023/SATS_Continual_Semantic_Seg

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SATS: Self-Attention Transfer for Continual Semantic Segmentation

https://github.com/QQTh4/NAS4Ultrasound

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SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

https://github.com/QTJiebin/MAD_Segmentation

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Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition

https://github.com/QiZhangsama/P-MSDiff

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P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation

https://github.com/QianWangX/VidSeg_diffusion

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Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models

https://github.com/Qianyu1226/Crosslink-Net

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Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

https://github.com/Qiming-Huang/ssformer

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Stepwise Feature Fusion: Local Guides Global

https://github.com/QingtaoPan/DuSSS

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

https://github.com/QingyongHu/RandLA-Net

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RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

https://github.com/QingyongHu/SQN

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SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds

https://github.com/QingyongHu/SensatUrban

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Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

https://github.com/Qinying-Liu/TagAlign

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TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification

https://github.com/QiujieDong/Laplacian2Mesh

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Laplacian2Mesh: Laplacian-Based Mesh Understanding

https://github.com/Qualcomm-AI-research/delta-distillation

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Delta Distillation for Efficient Video Processing

https://github.com/REAILAB/.github

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SolarSAM: Building-scale Photovoltaic Potential Assessment Based on Segment Anything Model (SAM) and Remote Sensing for Emerging City

https://github.com/RK621/ThreeStageSelftraining_SemanticSegmentation

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A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation

https://github.com/RL4M/PCRLv2

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PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

https://github.com/RPL-CS-UCL/UCL-AffCorrs

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One-Shot Transfer of Affordance Regions? AffCorrs!

https://github.com/RS-CSU/MemoryAdaptNet-master

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Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory

https://github.com/Radar-Camera-Fusion/Awesome-Radar-Camera-Fusion

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Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

https://github.com/RanSuLab/RAUNet-tumor-segmentation

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RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

https://github.com/RayYoh/SGIFormer

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SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation

https://github.com/Rayicer/TransFuse

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TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

https://github.com/Raykoooo/CAC-UNet

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Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

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