Stars
Separate mask2former from framework
MegaFormer-Tang Wuyang's undergraduate graduation design.
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
Multi-scale convolutional attention frequency-enhanced transformer network for medical image segmentation
pytorch implementation of SEG-GRAD-CAM,which based on grad-cam
deep learning for image processing including classification and object-detection etc.
Adding SAM to in-context learning on medical imaging segmentation
Try to use the SAM-ViT as the backbone to create the learnable prompt for semantic segmentation
Segment Anything Model for Medical Image Segmentation: Open-Source Project Summary
Online !!! Application of an efficient transformer improved based on Swin transformer on remote sensing segmentation
[NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, Cont…
Official repository of the "Active Learning for Semantic Segmentation with Multi-class Label Query (NeurIPS'23)"
[TPAMI]CTNet: Context-based Tandem Network for Semantic Segmentation
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
This code is for cross-domain segmentation tasks, which can plot T-sne of domains and classes
CycleGAN used for multimodal translation in medical imaging
MICCAI 2021 : Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation (Pytorch implementation).
PyTorch implementation of the InfoNCE loss for self-supervised learning.
Segment Anything in Medical Images
Prior Knowledge Guided Unsupervised Domain Adaptation (ECCV 2022)
PyTorch implementation of: Michieli U. and Zanuttigh P., "Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations", CVPR 2021.