https://github.com/huiimin5/aua
공식 코드Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
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Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Pseudo-label Alignment for Semi-supervised Instance Segmentation
You Only Segment Once: Towards Real-Time Panoptic Segmentation
EasyPortrait -- Face Parsing and Portrait Segmentation Dataset
MUXConv: Information Multiplexing in Convolutional Neural Networks
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
Few Shot Medical Image Segmentation with Cross Attention Transformer
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation
Crossover Learning for Fast Online Video Instance Segmentation
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels
Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation
Sparse Instance Activation for Real-Time Instance Segmentation
Temporally Efficient Vision Transformer for Video Instance Segmentation
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
A General Pipeline for Glomerulus Whole-Slide Image Segmentation
Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.