Papers with Code 아카이브

Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

5

보존된 평가 기록

6

코드

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

https://github.com/yulequan/UA-MT

공식 코드

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

https://github.com/yun-liu/MiniSeg

공식 코드

MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

https://github.com/yurongchen1998/yurong-lib

공식 코드

Towards to Robust and Generalized Medical Image Segmentation Framework

https://github.com/yuta-hi/bayesian_unet

공식 코드

Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling

https://github.com/yutao1008/mswin

공식 코드

Self-attention on Multi-Shifted Windows for Scene Segmentation

https://github.com/yuwenlo/hardnet-dfus

공식 코드

HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation

https://github.com/yuxiaodonghri/soit

공식 코드

SOIT: Segmenting Objects with Instance-Aware Transformers

https://github.com/yuyangsheng/surgical-desam

공식 코드

Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery

https://github.com/yuyue2uofa/simicl

공식 코드

A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation

https://github.com/ywher/TUFL

공식 코드

Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation

https://github.com/ywher/ss-ada

공식 코드

SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation

https://github.com/yxchng/mask-grounding

공식 코드

Mask Grounding for Referring Image Segmentation

https://github.com/yxfansuda/gptfordda

공식 코드

Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study

https://github.com/yxiwang/atp

공식 코드

A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation

https://github.com/yyNoBug/VAE_segmentation

공식 코드

Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation

https://github.com/yykzjh/pmfsnet

공식 코드

PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation

https://github.com/yyliu01/rpl

공식 코드

Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

https://github.com/yyliu01/tracoco

공식 코드

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/yyxssm/pointssc

공식 코드

PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion

https://github.com/yzd-v/MGD

공식 코드

Masked Generative Distillation

https://github.com/yzhangcs/crfpar

공식 코드

Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinese

https://github.com/yzhn16/imos

공식 코드

A Foundation Model for General Moving Object Segmentation in Medical Images

https://github.com/yzluka/annotationefficiency2d

공식 코드

How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model

https://github.com/z-x-yang/AOT

공식 코드

Scalable Video Object Segmentation with Identification Mechanism

https://github.com/z-x-yang/CFBI

공식 코드

Collaborative Video Object Segmentation by Foreground-Background Integration

https://github.com/zadaianchuk/comus

공식 코드

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations

https://github.com/zahrag/BRLVBVC

공식 코드

A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control

https://github.com/zaiweizhang/path_invariance_map_network

공식 코드

Path-Invariant Map Networks

https://github.com/zamling/psalm

공식 코드

PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model

https://github.com/zang0902/EM-Net

공식 코드

EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation

https://github.com/zaplm/dc-sam

공식 코드

DC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency

https://github.com/zarhult/hsda

공식 코드

HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation

https://github.com/zbf1991/A2GNN

공식 코드

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation

https://github.com/zbf1991/RRM

공식 코드

Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach

https://github.com/zbf1991/SCL

공식 코드

Self-Guided and Cross-Guided Learning for Few-Shot Segmentation

https://github.com/zbf1991/weclip

공식 코드

Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation

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