Python Semi-supervised-segmentation Resources

When Does Self-Supervision Help Graph Convolutional Networks? PyTorch implementation for When Does Self-Supervision Help Graph Convolutional Networks?

Feedback Convolutional Neural Network for Visual Localization and Segmentation The code is an implementation of Feedback Convolutional Neural Network

PointRend A PyTorch implementation of PointRend: Image Segmentation as Rendering [arxiv] [Official Implementation: Detectron2] This repo for Only Sema

Unet network with mean teacher for altrasound image segmentation

Video Object Segmentation(VOS) From Zero to Hero! Goal 1:train a two layers cnn model for vos. Finish! see model.py FFNet for more diteal.(2021.9.30)

BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa

What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Preprocess file of the dataset used in implicit sub-populations: (Demographic groups

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

LDBE Pytorch implementation for two papers (the paper will be released soon): "Domain Adaptive Semantic Segmentation without Source Data", ACM MM2021.

SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021) Kun Wang, Zhenyu Zhang, Zhiqiang Yan, X

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create

Motif-based Graph Self-Supervised Learning for Molecular Property Prediction Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Se

Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning This repository is the official implementation of CARE.

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat

Swin Transformer for Semantic Segmentation of satellite images This repo contains the supported code and configuration files to reproduce semantic seg

CRFConv This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation" 1. Setup 1) Building c

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision Paper | Project page | Demo (Youtube) | Demo (Bilib

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation Paper Multi-Target Adversarial Frameworks for Domain Adaptation in

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021) Alexey Nekrasov*, Jonas Schult*, Or Litany, Bastian Leibe, Francis Engelmann Mix3D is

SubTab: Author: Talip Ucar ([email protected]) The official implementation of the paper, SubTab: Subsetting Features of Tabular Data for Self-Supervis

LIVECell dataset This document contains instructions of how to access the data associated with the submitted manuscript "LIVECell - A large-scale data

Unified-EPT Code for the ICCV 2021 Workshop paper: A Unified Efficient Pyramid Transformer for Semantic Segmentation. Installation Linux, CUDA=10.0,

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

basic_cv a basic code repository for basic task in CV(classification,detection,segmentation,tracking) classification generate dataset train predict de

Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

The Official PyTorch Implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Vanilla GCSL This repository contains a vanilla implementation of "Learning to Reach Goals via Iterated Supervised Learning" proposed by Dibya Gosh et

Fast Soft Color Segmentation

Video Autoencoder: self-supervised disentanglement of 3D structure and motion This repository contains the code (in PyTorch) for the model introduced

Midgut_oocyst_segmentation Perform oocyst segmentation in mercurochrome stained mosquito midguts This oocyst segmentation model also powers the webtoo

A 3D multi-modal medical image segmentation library in PyTorch We strongly believe in open and reproducible deep learning research. Our goal is to imp

Deep Multi-Magnification Network This repository provides training and inference codes for Deep Multi-Magnification Network published here. Deep Multi

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding PyTorch implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedd

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.

PFENet This is the implementation of our paper PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation that has been accepted to IEE

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

Unseen Object Amodal Instance Segmentation (UOAIS) Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee This

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

A curated (most recent) list of resources for Learning with Noisy Labels

Self-Supervised depth kalilia

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

PyTorch Project Template is being sponsored by the following tool; please help to support us by taking a look and signing up to a free trial PyTorch P

pytorch-fcn PyTorch implementation of Fully Convolutional Networks. Requirements pytorch = 0.2.0 torchvision = 0.1.8 fcn = 6.1.5 Pillow scipy tqdm

Convolutional Neural Network Visualizations This repository contains a number of convolutional neural network visualization techniques implemented in

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

English | 简体中文 PaddleSeg PaddleSeg has released the new version including the following features: Our team won the AutoNUE@CVPR 2021 challenge, where

Semi-supervised-learning-for-medical-image-segmentation. Recently, semi-supervised image segmentation has become a hot topic in medical image computin

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

About subwAI subwAI - a project for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation

Add filters (background blur, etc) to your webcam on Linux.

CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis

FIDNet_SemanticKITTI Motivation Implementing complicated network modules with only one or two points improvement on hardware is tedious. So here we pr

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Retina Blood Vessels Segmentation This is an implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional

Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co

An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021) Introduction This is an official pytorch implemen

pytorch-deeplab-resnet DeepLab resnet v2 model implementation in pytorch. The architecture of deepLab-ResNet has been replicated exactly as it is from

pytorch-fcn PyTorch implementation of Fully Convolutional Networks. Requirements pytorch = 0.2.0 torchvision = 0.1.8 fcn = 6.1.5 Pillow scipy tqdm

A PyTorch implementation of V-Net Vnet is a PyTorch implementation of the paper V-Net: Fully Convolutional Neural Networks for Volumetric Medical Imag

PiWiSe Pixel-wise segmentation on the VOC2012 dataset using pytorch. FCN SegNet PSPNet UNet RefineNet For a more complete implementation of segmentati

Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge Correlation Explanation (CorEx) is a topic model that yields rich topics tha

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

EfficientSegmentation Introduction EfficientSegmentation is an open source, PyTorch-based segmentation framework for 3D medical image. Features A whol

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning [MedIA or Arxiv] and [Demo] This repository pr

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

Streaming speaker diarization Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation by Juan Manuel Coria, Hervé

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds This is the official code implementation for the paper "Spatio-temporal Se

End-to-End Semi-Supervised Object Detection with Soft Teacher By Mengde Xu*, Zheng Zhang*, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai,

Polyp-PVT by Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, & Ling Shao. This repo is the official implementation of "Polyp-PVT: Polyp Se

JOINT This is the official implementation of Joint Inductive and Transductive learning for Video Object Segmentation, to appear in ICCV 2021. @inproce

Streaming speaker diarization Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation by Juan Manuel Coria, Hervé

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019) Introduction Official implementation of Dynamic Multi-scale Filters for Semant

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation The code of: Cross-Image Region Mining with Region Proto

语义分割学习实验-基于VOC数据集 usage： 下载VOC数据集，将JPEGImages SegmentationClass两个文件夹放入到data文件夹下。 终端切换到目标目录，运行python train.py -h查看训练 (torch) qust116-jq@qustx-X299-WU8:

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Pre-trained (foundation) models across tasks (understanding, generation and translation), languages (100+ languages), and modalities (language, image, audio, vision + language, audio + language, etc.)

BASNet Boundary-Aware Segmentation Network for Mobile and Web Applications This repository contain implementation of BASNet in tensorflow/keras. comme

End-to-end neural table-text understanding models.

✨A Python framework to explore, label, and monitor data for NLP projects