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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awesome-MIM

Self-supervied Vision Transformers as backbone models.

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ligang

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PyTorch implementation of EfficientNetV2

Lane follower: Lane-detector (OpenCV) + Object-detector (YOLO5) + CAN-bus

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Active Offline Policy Selection With Python

NBEATSx: Neural basis expansion analysis with exogenous variables

[AAAI-2022] Official implementations of MCL: Mutual Contrastive Learning for Visual Representation Learning

Python interface for the DIGIT tactile sensor

Puzzle-CAM: Improved localization via matching partial and full features.

Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

Implementation of Bottleneck Transformer in Pytorch

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Deep Residual Learning for Image Recognition

Try out deep learning models online on Google Colab

Deep Learning applied to Integral data analysis

[NeurIPS'20] Multiscale Deep Equilibrium Models

Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)