Papers with Code 아카이브

Semantic Segmentation

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하위 태스크

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150

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코드

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

https://github.com/HealthX-Lab/MedCLIP-SAM

공식 코드

MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation

https://github.com/HeatherJiaZG/SuperGlue-pytorch

공식 코드

Physarum Powered Differentiable Linear Programming Layers and Applications

https://github.com/HelenMa9998/Selective_Uncertainty_AL

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Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation

https://github.com/HeliosZhao/NCDSS

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Novel Class Discovery in Semantic Segmentation

https://github.com/HengyiWang/SIRNet

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Boosting Video Object Segmentation based on Scale Inconsistency

https://github.com/HenrryBryant/Scene-Recognition-in-3D

공식 코드

Indoor Scene Recognition in 3D

https://github.com/HiLab-git/ACELoss

공식 코드

Learning Euler's Elastica Model for Medical Image Segmentation

https://github.com/HiLab-git/CA-Net

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CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

https://github.com/HiLab-git/DTC

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Semi-supervised Medical Image Segmentation through Dual-task Consistency

https://github.com/HiLab-git/SSL4MIS

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Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/HiLab-git/WSL4MIS

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Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision

https://github.com/HoangTrinh/EDMS

공식 코드

Image Compression with Encoder-Decoder Matched Semantic Segmentation

https://github.com/HolmesShuan/Location-aware-Upsampling-for-Semantic-Segmentation

공식 코드

Location-aware Upsampling for Semantic Segmentation

https://github.com/Howal/DNL-Object-Detection

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Disentangled Non-Local Neural Networks

https://github.com/HuCaoFighting/Swin-Unet

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Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

https://github.com/HuXiaoling/TopoLoss

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Topology-Preserving Deep Image Segmentation

https://github.com/Hua-YS/Semantic-Segmentation-with-Sparse-Labels

공식 코드

Semantic Segmentation of Remote Sensing Images with Sparse Annotations

https://github.com/HuguesTHOMAS/KPConv

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KPConv: Flexible and Deformable Convolution for Point Clouds

https://github.com/Hydragon516/PMN

공식 코드

Unsupervised Video Object Segmentation via Prototype Memory Network

https://github.com/HzFu/AGNet

공식 코드

Attention Guided Network for Retinal Image Segmentation

https://github.com/HzFu/COVID19_imaging_AI_paper_list

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Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

https://github.com/IBBM/Cascaded-FCN

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Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

https://github.com/ICEORY/PMF

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EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

https://github.com/IGNF/FLAIR-1-AI-Challenge

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FLAIR #1: semantic segmentation and domain adaptation dataset

https://github.com/IMPLabUniPr/mmdetection

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Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing

https://github.com/IMPLabUniPr/swin2-mose

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Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing

https://github.com/IRMVLab/SemGauss-SLAM

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SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM

https://github.com/IRVLUTD/FewSOL-DataLoader

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FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments

https://github.com/IRVLUTD/fewsol-toolkit

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FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments

https://github.com/IRVLUTD/meta-dataset

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FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments

https://github.com/ISosnovik/nn4topopt

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Neural networks for topology optimization

https://github.com/ISosnovik/top

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Neural networks for topology optimization

https://github.com/IVIPLab/FBSNet

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FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

https://github.com/IkeaASM/IKEA_ASM_Dataset

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The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose

https://github.com/Inha-CVAI/MADGNet

공식 코드

Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention

https://github.com/InnovArul/vidreid_cosegmentation

공식 코드

Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks

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