Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LifengFan/Triadic-Belief-Dynamics
공식 코드Learning Triadic Belief Dynamics in Nonverbal Communication from Videos
https://github.com/LingyiHongfd/LVOS
공식 코드https://github.com/LiuTingWed/CriDiff
공식 코드https://github.com/LiuZhu-CV/BDLFusion
공식 코드Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
https://github.com/LongchaoDa/SegmentAsYouWish
공식 코드Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images
https://github.com/Luffy03/DCA
공식 코드Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation
https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning
공식 코드https://github.com/MASILab/SynSeg-Net
공식 코드SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
https://github.com/MECLabTUDA/ACS
공식 코드Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
https://github.com/MECLabTUDA/Lifelong-nnUNet
공식 코드https://github.com/MECLabTUDA/M3D-NCA
공식 코드M3D-NCA: Robust 3D Segmentation with Built-in Quality Control
https://github.com/MECLabTUDA/SASVi
공식 코드https://github.com/MECLabTUDA/VoxelSceneGraph
공식 코드https://github.com/MIC-DKFZ/MedNeXt
공식 코드MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
https://github.com/MIC-DKFZ/nnDetection
공식 코드nnDetection: A Self-configuring Method for Medical Object Detection
https://github.com/MIC-DKFZ/nnunet
공식 코드nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
https://github.com/MIPT-Oulu/mCTSegmentation
공식 코드https://github.com/MIT-SPARK/Kimera
공식 코드Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
https://github.com/MMCheng/SSPC-Net
공식 코드SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
https://github.com/MSiam/AdaptiveMaskedProxies
공식 코드https://github.com/MSiam/motion_adaptation
공식 코드https://github.com/MVIG-SJTU/WSHP
공식 코드Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
https://github.com/MansoorehMontazerin/Livelayer-Software
공식 코드https://github.com/MartinPernus/MaskFaceGAN
공식 코드High Resolution Face Editing with Masked GAN Latent Code Optimization
https://github.com/MarvinLer/tcga_segmentation
공식 코드Weakly supervised multiple instance learning histopathological tumor segmentation
https://github.com/MarvinTeichmann/ConvCRF
공식 코드https://github.com/MathLee/OLBPNet4PFOS
공식 코드Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
https://github.com/MauritsBleeker/Bi-STET
공식 코드https://github.com/McGregorWwww/UDTransNet
공식 코드https://github.com/Media-Smart/vedastr
공식 코드Revisiting Classification Perspective on Scene Text Recognition
https://github.com/MegviiDetection/video_analyst
공식 코드https://github.com/MengzhangLI/ASNG
공식 코드Automatic Data Augmentation for 3D Medical Image Segmentation
https://github.com/Merrical/PADL
공식 코드Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
https://github.com/Merrical/TAB
공식 코드Transformer-based Annotation Bias-aware Medical Image Segmentation
https://github.com/MhLiao/MaskTextSpotter
공식 코드Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.