[IEEE TIP] Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

CVPR 2024: AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation

This repo includes ChatGPT prompt curation to use ChatGPT better.

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NeurIPS 2023: Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation

MICCAI 2023: DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

A distributed graph deep learning framework.

Official code for "CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation"

Not All Pixels Are Equal: Learning Hardness Probability for Semantic Segmentation.

[ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"

Multi-task Attention-based Semi-supervised Learning framework for image segmentation

Official implementation of FullMatch (CVPR2023)

For QRunes Compiler and Language tutorial

量子计算（Quantum Computing）

Official Code for NeurIPS 2022 Paper: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

A New Padding Scheme: Partial Convolution based Padding

A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks [ECCV 2020]

cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

segmentation paper reading notes

[CVPR 2023] Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation

Masked Siamese Networks for Label-Efficient Learning (https://arxiv.org/abs/2204.07141)

Per-Pixel Classification is Not All You Need for Semantic Segmentation (NeurIPS 2021, spotlight)

A simple consistency training framework for semi-supervised image semantic segmentation

Implementation of our Pattern Recognition paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

[NeurIPS'22] Learning from Future: A Novel Self-Training Framework for Semantic Segmentation.