Papers with Code 아카이브

Segmentation

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코드

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

https://github.com/Jacobew/AutoPanoptic

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Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

https://github.com/Jacobi93/Reward-penalty-Dice-loss

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Learning Non-Unique Segmentation with Reward-Penalty Dice Loss

https://github.com/Jarvis73/FPTransPaddle

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Feature-Proxy Transformer for Few-Shot Segmentation

https://github.com/JasonZuu/SegX

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SegX: Improving Interpretability of Clinical Image Diagnosis with Segmentation-based Enhancement

https://github.com/Javion11/PointLiBR

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Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentation

https://github.com/Jazzcharles/OVSegmentor

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Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

https://github.com/JeroenBertels/optimizingdice

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Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

https://github.com/Jia-Research-Lab/PanopticFCN

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Fully Convolutional Networks for Panoptic Segmentation

https://github.com/JiajieMo/OctaveUNet

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Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network

https://github.com/JialeCao001/SipMask

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SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation

https://github.com/JialianW/TraDeS

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Track to Detect and Segment: An Online Multi-Object Tracker

https://github.com/Jianbo-Lab/LBIE

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Language-Based Image Editing with Recurrent Attentive Models

https://github.com/JianghaoWu/FPL-UDA

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/JianqiangWan/Super-BPD

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Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

https://github.com/Jiaolong/self-supervised-da

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Self-supervised Domain Adaptation for Computer Vision Tasks

https://github.com/Jiaxuan-Li/MGU-Net

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Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

https://github.com/JieZheng-ShanghaiTech/CellVTA

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CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification

https://github.com/JimTheBarbarian/Official-TTV-Segmentation

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An Image Segmentation Model with Transformed Total Variation

https://github.com/Jin-Ying/GTA-Seg

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Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant

https://github.com/JinYuanLi0012/RiVEG

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Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based Segmentation

https://github.com/JindongJiang/RedNet

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RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation

https://github.com/JingchunCheng/SegFlow

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SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

https://github.com/JoHof/lungmask

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Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

https://github.com/JoetheReindeer/multiPI_TransBTS

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

https://github.com/JohnleeHIT/Brats2019

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Multi-step Cascaded Networks for Brain Tumor Segmentation

https://github.com/JojiJoseph/3dgs-gradient-backprojection

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Gradient-Weighted Feature Back-Projection: A Fast Alternative to Feature Distillation in 3D Gaussian Splatting

https://github.com/JojiJoseph/3dgs-gradient-segmentation

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Gradient-Driven 3D Segmentation and Affordance Transfer in Gaussian Splatting Using 2D Masks

https://github.com/JorisRoels/domain-adaptive-segmentation

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Domain Adaptive Segmentation in Volume Electron Microscopy Imaging

https://github.com/JoseLGomez/Co-training_SemSeg_UDA

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Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

https://github.com/JosephPB/XNet

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XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

https://github.com/Jun-Pu/UCOS-DA

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Unsupervised Camouflaged Object Segmentation as Domain Adaptation

https://github.com/JunMa11/ADAM2020

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Loss Ensembles for Extremely Imbalanced Segmentation

https://github.com/JunMa11/AbdomenCT-1K

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AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

https://github.com/JunMa11/CADA2020

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Exploring Large Context for Cerebral Aneurysm Segmentation

https://github.com/JunMa11/HM_DataAug

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Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation

https://github.com/JunMa11/SegLoss

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Segmentation Loss Odyssey

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