awesome-MIM

Reading list for research topics in Masked Image Modeling(MIM).

We list the most popular methods for MIM, if I missed something, please submit a request. (Note: We show the date of the first version of Arxiv here. But the link of paper may be not the early version.)

Self-supervied Vision Transformers as backbone models.

Date	Method	Conference	Title	Code
2021-06-14	BeiT	ICLR 2022(Oral)	BEiT: BERT Pre-Training of Image Transformers	BeiT
2021-11-11	MAE	Arxiv 2021	Masked Autoencoders Are Scalable Vision Learners	MAE
2021-11-15	iBoT	Arxiv 2021	iBOT: Image BERT Pre-Training with Online Tokenizer	iBoT
2021-11-18	SimMIM	Arxiv 2021	SimMIM: A Simple Framework for Masked Image Modeling	SimMIM
2021-12-16	MaskFeat	Arxiv 2021	Masked Feature Prediction for Self-Supervised Visual Pre-Training	None
2021-12-20	SplitMask	Arxiv 2021	Are Large-scale Datasets Necessary for Self-Supervised Pre-training?	None
2022-01-31	ADIOS	Arxiv 2022	Adversarial Masking for Self-Supervised Learning	None
2022-02-07	CAE	Arxiv 2022	Context Autoencoder for Self-Supervised Representation Learning	None
2022-02-07	CIM	Arxiv 2022	Corrupted Image Modeling for Self-Supervised Visual Pre-Training	None

Reading list for research topics in Masked Image Modeling

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Overview

awesome-MIM

Self-supervied Vision Transformers as backbone models.

Owner

ligang

Random-Afg - Afghanistan Random Old Idz Cloner Tools

This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22)

A robust pointcloud registration pipeline based on correlation.

Fast image augmentation library and an easy-to-use wrapper around other libraries

An official implementation of the Anchor DETR.

Cache Requests in Deta Bases and Echo them with Deta Micros

PyTorch implementation of Off-policy Learning in Two-stage Recommender Systems

Implementation of CaiT models in TensorFlow and ImageNet-1k checkpoints. Includes code for inference and fine-tuning.

Deep Learning for Human Part Discovery in Images - Chainer implementation

Implementing a simplified copy of Shazam application from scratch using MinHashing and LSH.

Semantic segmentation models, datasets and losses implemented in PyTorch.

ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

Classification Modeling: Probability of Default

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Code for the Lovász-Softmax loss (CVPR 2018)

Python Implementation of the CoronaWarnApp (CWA) Event Registration

This is a collection of our NAS and Vision Transformer work.

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

PyTorch code for our paper "Image Super-Resolution with Non-Local Sparse Attention" (CVPR2021).