Awesome Masked Modeling for Self-supervised Vision Represention and Beyond

Introduction

We summarize awesome Masked Image Modeling (MIM) and relevent Masked Modeling methods proposed for self-supervised representation learning. Welcome to add relevant masked modeling paper to our project!

This project is a part of our survey on masked modeling methods (arXiv). The list of awesome MIM methods is summarized in chronological order and is on updating. If you find any typos or any missed paper, please feel free to open an issue or send a pull request. Currently, our survey is on updating and here is the latest version.

• To find related papers and their relationships, check out Connected Papers, which visualizes the academic field in a graph representation.
• To export BibTeX citations of papers, check out arXiv or Semantic Scholar of the paper for professional reference formats.

Research in self-supervised learning can be broadly categorized into Generative and Discriminative paradigms. We reviewed major SSL research since 2008 and found that SSL has followed distinct developmental trajectories and stages across time periods and modalities. Since 2018, SSL in NLP has been dominated by generative masked language modeling, which remains mainstream. In computer vision, discriminative contrastive learning dominated from 2018 to 2021 before masked image modeling gained prominence after 2022.

Table of Contents

• Awesome Masked Modeling for Self-supervised Vision Represention and Beyond
  • Introduction
  • Table of Contents
  • Fundamental MIM Methods
    • MIM for Transformers
    • MIM with Constrastive Learning
    • MIM for Transformers and CNNs
    • MIM with Advanced Masking
    • MIM for Multi-Modality
    • MIM for Vision Generalist Model
    • Image Generation
  • MIM for CV Downstream Tasks
    • Object Detection
    • Video Rrepresentation
    • Knowledge Distillation and Few-shot Classification
    • Efficient Fine-tuning
    • Medical Image
    • Face Recognition
    • Scene Text Recognition (OCR)
    • Remote Sensing Image
    • 3D Representation Learning
    • Depth Estimation
  • Audio and Speech
  • AI for Science
    • Protein
    • Chemistry
    • Physics
  • Neuroscience Learning
  • Reinforcement Learning
  • Analysis and Understanding of Masked Modeling
  • Survey
  • Contribution
  • Related Project
    • Paper List of Masked Image Modeling
    • Project of Self-supervised Learning

Fundamental MIM Methods

The overview of the basic MIM framework, containing four building blocks with their internal components and functionalities. All MIM research can be summarized as innovations upon these four blocks, i.e., Masking, Encoder, Target, and Head. Frameworks of masked modeling in other modalities are similar to this framework.

MIM for Transformers

• Generative Pretraining from Pixels
  Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, David Luan, Ilya Sutskever
  ICML'2020 [Paper] [Code]

  iGPT Framework

  

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
  Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
  ICLR'2021 [Paper] [Code]

  ViT Framework

  

• BEiT: BERT Pre-Training of Image Transformers
  Hangbo Bao, Li Dong, Furu Wei
  ICLR'2022 [Paper] [Code]

  BEiT Framework

  

• iBOT: Image BERT Pre-Training with Online Tokenizer
  Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, Tao Kong
  ICLR'2022 [Paper] [Code]

  iBOT Framework

  

• Masked Autoencoders Are Scalable Vision Learners
  Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick
  CVPR'2022 [Paper] [Code]

  MAE Framework

  

• SimMIM: A Simple Framework for Masked Image Modeling
  Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, Han Hu
  CVPR'2022 [Paper] [Code]

  SimMIM Framework

  

• Masked Feature Prediction for Self-Supervised Visual Pre-Training
  Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, Christoph Feichtenhofer
  CVPR'2022 [Paper] [Code]

  MaskFeat Framework

  

• data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
  Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
  ICML'2022 [Paper] [Code]

  data2vec Framework

  

• Position Prediction as an Effective Pretraining Strategy
  Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram, Dan Busbridge, Tatiana Likhomanenko, Joseph Yitan Cheng, Walter Talbott, Chen Huang, Hanlin Goh, Joshua Susskind
  ICML'2022 [Paper]

  MP3 Framework

  

• PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
  Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu
  AAAI'2023 [Paper] [Code]

  PeCo Framework

  

• MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning
  Sara Atito, Muhammad Awais, Ammarah Farooq, Zhenhua Feng, Josef Kittler
  ArXiv'2021 [Paper]

  MC-SSL0.0 Framework

  

• mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
  Xiaotong Li, Yixiao Ge, Kun Yi, Zixuan Hu, Ying Shan, Ling-Yu Duan
  ECCV'2022 [Paper] [Code]

  mc-BEiT Framework

  

• Bootstrapped Masked Autoencoders for Vision BERT Pretraining
  Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu
  ECCV'2022 [Paper] [Code]

  BootMAE Framework

  

• SdAE: Self-distillated Masked Autoencoder
  Yabo Chen, Yuchen Liu, Dongsheng Jiang, Xiaopeng Zhang, Wenrui Dai, Hongkai Xiong, Qi Tian
  ECCV'2022 [Paper] [Code]

  SdAE Framework

  

• MultiMAE: Multi-modal Multi-task Masked Autoencoders
  Roman Bachmann, David Mizrahi, Andrei Atanov, Amir Zamir
  ECCV'2022 [Paper] [Code]

  MultiMAE Framework

  

• SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
  Feng Liang, Yangguang Li, Diana Marculescu
  ArXiv'2022 [Paper] [Code]

  SupMAE Framework

  

• MVP: Multimodality-guided Visual Pre-training
  Longhui Wei, Lingxi Xie, Wengang Zhou, Houqiang Li, Qi Tian
  ArXiv'2022 [Paper]

  MVP Framework

  

• The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training
  Hao Liu, Xinghua Jiang, Xin Li, Antai Guo, Deqiang Jiang, Bo Ren
  AAAI'2023 [Paper]

  Ge2AE Framework

  

• ConvMAE: Masked Convolution Meets Masked Autoencoders
  Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, Yu Qiao
  NeurIPS'2022 [Paper] [Code]

  ConvMAE Framework

  

• Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking
  Peng Gao, Renrui Zhang, Rongyao Fang, Ziyi Lin, Hongyang Li, Hongsheng Li, Qiao Yu
  arXiv'2023 [Paper] [Code]

  MR-MAE (ConvMAE.V2) Framework

  

• Green Hierarchical Vision Transformer for Masked Image Modeling
  Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Toshihiko Yamasaki
  NeurIPS'2022 [Paper] [Code]

  GreenMIM Framework

  

• Test-Time Training with Masked Autoencoders
  Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros
  NeurIPS'2022 [Paper] [Code]

  TTT-MAE Framework

  

• HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
  Xiaosong Zhang, Yunjie Tian, Wei Huang, Qixiang Ye, Qi Dai, Lingxi Xie, Qi Tian
  ICLR'2023 [Paper]

  HiViT Framework

  

• Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
  Yixuan Wei, Han Hu, Zhenda Xie, Zheng Zhang, Yue Cao, Jianmin Bao, Dong Chen, Baining Guo
  ArXiv'2022 [Paper] [Code]

  FD Framework

  

• Object-wise Masked Autoencoders for Fast Pre-training
  Jiantao Wu, Shentong Mo
  ArXiv'2022 [Paper]

  ObjMAE Framework

  

• Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction
  Jun Chen, Ming Hu, Boyang Li, Mohamed Elhoseiny
  ArXiv'2022 [Paper] [Code]

  LoMaR Framework

  

• Extreme Masking for Learning Instance and Distributed Visual Representations
  Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin
  ArXiv'2022 [Paper]

  ExtreMA Framework

  

• BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
  Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, Furu Wei
  ArXiv'2022 [Paper] [Code]

  BEiT.V2 Framework

  

• MILAN: Masked Image Pretraining on Language Assisted Representation
  Zejiang Hou, Fei Sun, Yen-Kuang Chen, Yuan Xie, Sun-Yuan Kung
  ArXiv'2022 [Paper] [Code]

  MILAN Framework

  

• Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders
  Youngwan Lee, Jeffrey Willette, Jonghee Kim, Juho Lee, Sung Ju Hwang
  ICLR'2023 [Paper]

  RC-MAE Framework

  

• Denoising Masked AutoEncoders are Certifiable Robust Vision Learners
  Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He
  ArXiv'2022 [Paper] [Code]

  DMAE Framework

  

• A Unified View of Masked Image Modeling
  Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, Furu Wei
  ArXiv'2022 [Paper] [Code]

  MaskDistill Framework

  

• DILEMMA: Self-Supervised Shape and Texture Learning with Transformers
  Sepehr Sameni, Simon Jenni, Paolo Favaro
  AAAI'2023 [Paper]

  DILEMMA Framework

  

• i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable
  Kevin Zhang, Zhiqiang Shen
  ArXiv'2022 [Paper] [Code]

  i-MAE Framework

  

• EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
  Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao
  CVPR'2023 [Paper] [Code]

  EVA Framework

  

• EVA-02: A Visual Representation for Neon Genesis
  Yuxin Fang, Quan Sun, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao
  CVPR'2024 [Paper] [Code]

  EVA-02 Framework

  

• Context Autoencoder for Self-Supervised Representation Learning
  Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, Jingdong Wang
  IJCV'2023 [Paper] [Code]

  CAE Framework

  

• CAE v2: Context Autoencoder with CLIP Target
  Xinyu Zhang, Jiahui Chen, Junkun Yuan, Qiang Chen, Jian Wang, Xiaodi Wang, Shumin Han, Xiaokang Chen, Jimin Pi, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang
  ArXiv'2022 [Paper]

  CAE.V2 Framework

  

• FastMIM: Expediting Masked Image Modeling Pre-training for Vision
  Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Yunhe Wang, Chang Xu
  ArXiv'2022 [Paper]

  FastMIM Framework

  

• Exploring Target Representations for Masked Autoencoders
  Xingbin Liu, Jinghao Zhou, Tao Kong, Xianming Lin, Rongrong Ji
  ArXiv'2022 [Paper] [Code]

  dBOT Framework

  

• Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
  Alexei Baevski, Arun Babu, Wei-Ning Hsu, and Michael Auli
  ICML'2023 [Paper] [Code]

  Data2Vec.V2 Framework

  

• Masked autoencoders are effective solution to transformer data-hungry
  Jiawei Mao, Honggu Zhou, Xuesong Yin, Yuanqi Chang. Binling Nie. Rui Xu
  ArXiv'2022 [Paper] [Code]

  SDMAE Framework

  

• TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
  Sucheng Ren, Fangyun Wei, Zheng Zhang, Han Hu
  ArXiv'2023 [Paper] [Code]

  TinyMIM Framework

  

• Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
  Xin Ma, Chang Liu, Chunyu Xie, Long Ye, Yafeng Deng, Xiangyang Ji
  ArXiv'2023 [Paper] [Code]

  DMJD Framework

  

• Mixed Autoencoder for Self-supervised Visual Representation Learning
  Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung
  CVPR'2023 [Paper]

  MixedAE Framework

  

• Masked Image Modeling with Local Multi-Scale Reconstruction
  Haoqing Wang, Yehui Tang, Yunhe Wang, Jianyuan Guo, Zhi-Hong Deng, Kai Han
  CVPR'2023 [Paper] [Code]

  LocalMAE Framework

  

• Stare at What You See: Masked Image Modeling without Reconstruction
  Hongwei Xue, Peng Gao, Hongyang Li, Yu Qiao, Hao Sun, Houqiang Li, Jiebo Luo
  CVPR'2023 [Paper] [Code]

  MaskAlign Framework

  

• Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
  Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas
  CVPR'2023 [Paper]

  I-JEPA Framework

  

• MOMA: Distill from Self-Supervised Teachers
  Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
  arXiv'2023 [Paper]

  MOMA Framework

  

• PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
  Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin
  arXiv'2023 [Paper] [Code]

  PixMIM Framework

  

• Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
  Heng Pan, Chenyang Liu, Wenxiao Wang, Li Yuan, Hongfa Wang, Zhifeng Li, Wei Liu
  arXiv'2023 [Paper]

  Img2Vec Framework

  

• A Closer Look at Self-Supervised Lightweight Vision Transformers
  Shaoru Wang, Jin Gao, Zeming Li, Xiaoqin Zhang, Weiming Hu
  ICML'2023 [Paper] [Code]

  MAE-Lite Framework

  

• Architecture-Agnostic Masked Image Modeling - From ViT back to CNN
  Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Stan.Z.Li
  ICML'2023 [Paper] [Code] [project]

  A2MIM Framework

  

• Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
  Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer
  ICML'2023 [Paper] [Code]

  Hiera Framework

  

• The effectiveness of MAE pre-pretraining for billion-scale pretraining
  Mannat Singh, Quentin Duval, Kalyan Vasudev Alwala, Haoqi Fan, Vaibhav Aggarwal, Aaron Adcock, Armand Joulin, Piotr Dollár, Christoph Feichtenhofer, Ross Girshick, Rohit Girdhar, Ishan Misra
  ICCV'2023 [Paper]

  WSP Framework

  

• Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training
  Lorenzo Baraldi, Roberto Amoroso, Marcella Cornia, Lorenzo Baraldi, Andrea Pilzer, Rita Cucchiara
  ArXiv'2023 [Paper] [Code]

  MaPeT Framework

  

• R-MAE: Regions Meet Masked Autoencoders
  Duy-Kien Nguyen, Vaibhav Aggarwal, Yanghao Li, Martin R. Oswald, Alexander Kirillov, Cees G. M. Snoek, Xinlei Chen
  ICLR'2024 [Paper] [Code]

  R-MAE Framework

  

• Improving Pixel-based MIM by Reducing Wasted Modeling Capability
  Yuan Liu, Songyang Zhang, Jiacheng Chen, Zhaohui Yu, Kai Chen, Dahua Lin
  ICCV'2023 [Paper] [Code]

  MFM Framework

  

• SparseMAE: Sparse Training Meets Masked Autoencoders
  Aojun Zhou, Yang Li, Zipeng Qin, Jianbo Liu, Junting Pan, Renrui Zhang, Rui Zhao, Peng Gao, Hongsheng Li
  ICCV'2023 [Paper] [Code]

  SparseMAE Framework

  

• Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting
  Qidong Huang, Xiaoyi Dong, Dongdong Chen, Yinpeng Chen, Lu Yuan, Gang Hua, Weiming Zhang, Nenghai Yu
  ICCV'2023 [Paper] [Code]

  RobustMAE Framework

  

• DeepMIM: Deep Supervision for Masked Image Modeling
  Sucheng Ren, Fangyun Wei, Samuel Albanie, Zheng Zhang, Han Hu
  arXiv'2023 [Paper] [Code]

  RobustMAE Framework

  

• Rethinking Patch Dependence for Masked Autoencoders
  Letian Fu, Long Lian, Renhao Wang, Baifeng Shi, Xudong Wang, Adam Yala, Trevor Darrell, Alexei A. Efros, Ken Goldberg
  ArXiv'2024 [Paper]

  CrossMAE Framework

  

• Deconstructing Denoising Diffusion Models for Self-Supervised Learning
  Xinlei Chen, Zhuang Liu, Saining Xie, Kaiming He
  ArXiv'2024 [Paper]

  l-DAE Framework

  

• Denoising Autoregressive Representation Learning
  Yazhe Li, Jorg Bornschein, Ting Chen
  ArXiv'2024 [Paper]

  DARL Framework

  

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MIM with Constrastive Learning

• MST: Masked Self-Supervised Transformer for Visual Representation
  Zhaowen Li, Zhiyang Chen, Fan Yang, Wei Li, Yousong Zhu, Chaoyang Zhao, Rui Deng, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang
  NeurIPS'2021 [Paper]

  MST Framework

  

• Are Large-scale Datasets Necessary for Self-Supervised Pre-training
  Alaaeldin El-Nouby, Gautier Izacard, Hugo Touvron, Ivan Laptev, Hervé Jegou, Edouard Grave
  ArXiv'2021 [Paper]

  SplitMask Framework

  

• Masked Siamese Networks for Label-Efficient Learning
  Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas
  ArXiv'2022 [Paper] [Code]

  MSN Framework

  

• Siamese Image Modeling for Self-Supervised Vision Representation Learning
  Chenxin Tao, Xizhou Zhu, Gao Huang, Yu Qiao, Xiaogang Wang, Jifeng Dai
  ArXiv'2022 [Paper] [Code]

  SIM Framework

  

• Masked Contrastive Representation Learning
  Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
  ArXiv'2022 [Paper]

  MACRL Framework

  

• Masked Image Modeling with Denoising Contrast
  Kun Yi, Yixiao Ge, Xiaotong Li, Shusheng Yang, Dian Li, Jianping Wu, Ying Shan, Xiaohu Qie
  ICLR'2023 [Paper] [Code]

  ConMIM Framework

  

• RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training
  Luya Wang, Feng Liang, Yangguang Li, Honggang Zhang, Wanli Ouyang, Jing Shao
  ArXiv'2022 [Paper]

  RePre Framework

  

• Masked Siamese ConvNets
  Li Jing, Jiachen Zhu, Yann LeCun
  ArXiv'2022 [Paper]

  MSCN Framework

  

• Contrastive Masked Autoencoders are Stronger Vision Learners
  Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, Jiashi Feng
  ArXiv'2022 [Paper] [Code]

  CMAE Framework

  

• A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
  Shlok Mishra, Joshua Robinson, Huiwen Chang, David Jacobs, Aaron Sarna, Aaron Maschinot, Dilip Krishnan
  ArXiv'2022 [Paper]

  CAN Framework

  

• MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
  Qiang Zhou, Chaohui Yu, Hao Luo, Zhibin Wang, Hao Li
  ArXiv'2022 [Paper]

  MimCo Framework

  

• Contextual Image Masking Modeling via Synergized Contrasting without View Augmentation for Faster and Better Visual Pretraining
  Shaofeng Zhang, Feng Zhu, Rui Zhao, Junchi Yan
  ICLR'2023 [Paper] [Code]

  ccMIM Framework

  

• How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
  Qi Zhang, Yifei Wang, Yisen Wang
  NeurIPS'2022 [Paper] [Code]

  U-MAE Framework

  

• Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations
  Ziyu Jiang, Yinpeng Chen, Mengchen Liu, Dongdong Chen, Xiyang Dai, Lu Yuan, Zicheng Liu, Zhangyang Wang
  ICLR'2023 [Paper] [Code]

  Layer Grafted Framework

  

• DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
  Haochen Wang, Junsong Fan, Yuxi Wang, Kaiyou Song, Tong Wang, Zhaoxiang Zhang
  NeurIPS'2023 [Paper] [Code]

  DropPos Framework

  

• Rejuvenating image-GPT as Strong Visual Representation Learners
  Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan Yuille, Cihang Xie
  arXiv'2023 [Paper] [Code]

  D-iGPT Framework

  

• CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets
  Jiange Yang, Sheng Guo, Gangshan Wu, Limin Wang
  AAAI'2023 [Paper] [Code]

  CoMAE Framework

  

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MIM for Transformers and CNNs

• Context Encoders: Feature Learning by Inpainting
  Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros
  CVPR'2016 [Paper] [Code]

  Context-Encoder Framework

  

• Corrupted Image Modeling for Self-Supervised Visual Pre-Training
  Yuxin Fang, Li Dong, Hangbo Bao, Xinggang Wang, Furu Wei
  ICLR'2023 [Paper]

  CIM Framework

  

• Architecture-Agnostic Masked Image Modeling - From ViT back to CNN
  Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Stan.Z.Li
  ICML'2023 [Paper] [Code] [project]

  A2MIM Framework

  

• Masked Frequency Modeling for Self-Supervised Visual Pre-Training
  Jiahao Xie, Wei Li, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy
  ICLR'2023 [Paper] [Code]

  MFM Framework

  

• MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
  Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li
  CVPR'2023 [Paper] [Code]

  MixMAE Framework

  

• Masked Autoencoders are Robust Data Augmentors
  Haohang Xu, Shuangrui Ding, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
  ArXiv'2022 [Paper] [Code]

  MRA Framework

  

• Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
  Keyu Tian, Yi Jiang, Qishuai Diao, Chen Lin, Liwei Wang, Zehuan Yuan
  ICLR'2023 [Paper] [Code]

  SparK Framework

  

• ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
  Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie
  CVPR'2023 [Paper] [Code]

  ConvNeXt.V2 Framework

  

• RevColV2: Exploring Disentangled Representations in Masked Image Modeling
  Qi Han, Yuxuan Cai, Xiangyu Zhang
  NeurIPS'2023 [Paper] [Code]

  RevCol.V2 Framework

  

• Masked Capsule Autoencoders
  Miles Everett, Mingjun Zhong, Georgios Leontidis
  arXiv'2024 [Paper]

  MCAE Framework

  

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MIM with Advanced Masking

• MST: Masked Self-Supervised Transformer for Visual Representation
  Zhaowen Li, Zhiyang Chen, Fan Yang, Wei Li, Yousong Zhu, Chaoyang Zhao, Rui Deng, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang
  NeurIPS'2021 [Paper]

  MST Framework

  

• Adversarial Masking for Self-Supervised Learning
  Yuge Shi, N. Siddharth, Philip H.S. Torr, Adam R. Kosiorek
  ICML'2022 [Paper] [Code]

  ADIOS Framework

  

• What to Hide from Your Students: Attention-Guided Masked Image Modeling
  Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos, Nikos Komodakis
  ECCV'2022 [Paper] [Code]

  AttMask Framework

  

• Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
  Xiang Li, Wenhai Wang, Lingfeng Yang, Jian Yang
  ArXiv'2022 [Paper] [Code]

  UnMAE Framework

  

• SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
  Gang Li, Heliang Zheng, Daqing Liu, Chaoyue Wang, Bing Su, Changwen Zheng
  NeurIPS'2022 [Paper] [Code]

  SemMAE Framework

  

• Good helper is around you: Attention-driven Masked Image Modeling
  Zhengqi Liu, Jie Gui, Hao Luo
  AAAI'2023 [Paper] [Code]

  AMT Framework

  

• Hard Patches Mining for Masked Image Modeling
  Haochen Wang, Kaiyou Song, Junsong Fan, Yuxi Wang, Jin Xie, Zhaoxiang Zhang
  CVPR'2023 [Paper] [Code]

  HPM Framework

  

• AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
  Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami, Mehdi Nikkhah, Motilal Agrawal, Vishal M. Patel
  CVPR'2023 [Paper] [Code]

  AdaMAE Framework

  

• Improving Masked Autoencoders by Learning Where to Mask
  Haijian Chen, Wendong Zhang, Yunbo Wang, Xiaokang Yang
  arXiv'2023 [Paper]

  AutoMAE Framework

  

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MIM for Multi-Modality

• VL-BERT: Pre-training of Generic Visual-Linguistic Representations
  Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai
  ICLR'2020 [Paper] [Code]

  VL-BERT Framework

  

• MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
  Xiaoyi Dong, Jianmin Bao, Yinglin Zheng, Ting Zhang, Dongdong Chen, Hao Yang, Ming Zeng, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu
  CVPR'2023 [Paper] [Code]

  MaskCLIP Framework

  

• Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
  Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, Aniruddha Kembhavi
  ArXiv'2022 [Paper] [Code]

  Unified-IO Framework

  

• Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
  Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, Furu Wei
  ArXiv'2022 [Paper] [Code]

  BEiT.V3 Framework

  

• Masked Vision and Language Modeling for Multi-modal Representation Learning
  Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran, Erhan Bas, Rahul Bhotika, Stefano Soatto
  ICLR'2023 [Paper]

  MaskVLM Framework

  

• Scaling Language-Image Pre-training via Masking
  Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, Kaiming He
  CVPR'2023 [Paper]

  FLIP Framework

  

• All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
  Jia Ning, Chen Li, Zheng Zhang, Zigang Geng, Qi Dai, Kun He, Han Hu
  arXiv'2023 [Paper]

  AiT Framework

  

• Attentive Mask CLIP
  Yifan Yang, Weiquan Huang, Yixuan Wei, Houwen Peng, Xinyang Jiang, Huiqiang Jiang, Fangyun Wei, Yin Wang, Han Hu, Lili Qiu, Yuqing Yang
  ICCV'2023 [Paper]

  A-CLIP Framework

  

• MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
  Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, Kai Chen
  arXiv'2023 [Paper] [Code]

  MultiModal-GPT Framework

  

• VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
  Jinguo Zhu, Xiaohan Ding, Yixiao Ge, Yuying Ge, Sijie Zhao, Hengshuang Zhao, Xiaohua Wang, Ying Shan
  arXiv'2023 [Paper] [Code]

  VL-GPT Framework

  

• Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
  Jiasen Lu, Christopher Clark, Sangho Lee, Zichen Zhang, Savya Khosla, Ryan Marten, Derek Hoiem, Aniruddha Kembhavi
  ArXiv'2023 [Paper] [Code]

  Unified-IO 2 Framework

  

MIM for Vision Generalist Model

• Images Speak in Images: A Generalist Painter for In-Context Visual Learning
  Xinlong Wang, Wen Wang, Yue Cao, Chunhua Shen, Tiejun Huang
  CVPR'2023 [Paper] [Code]

  Painter Framework

  

• InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
  Yulu Gan, Sungwoo Park, Alexander Schubert, Anthony Philippakis, Ahmed M. Alaa
  arXiv'2023 [Paper] [Code]

  InstructCV Framework

  

• InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
  Zigang Geng, Binxin Yang, Tiankai Hang, Chen Li, Shuyang Gu, Ting Zhang, Jianmin Bao, Zheng Zhang, Han Hu, Dong Chen, Baining Guo
  arXiv'2023 [Paper]

  InstructDiffusion Framework

  

• Sequential Modeling Enables Scalable Learning for Large Vision Models
  Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan Yuille, Trevor Darrell, Jitendra Malik, Alexei A Efros
  arXiv'2023 [Paper] [Code]

  LVM Framework

  

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Image Generation

• Discrete Variational Autoencoders
  Jason Tyler Rolfe
  ICLR'2017 [Paper] [Code]

• Neural Discrete Representation Learning
  Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu
  NeurIPS'2017 [Paper] [Code]

• Theory and Experiments on Vector Quantized Autoencoders (EM VQ-VAE)
  Aurko Roy, Ashish Vaswani, Arvind Neelakantan, Niki Parmar
  Arxiv'2018 [Paper] [Code]

• DVAE: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
  Arash Vahdat, Evgeny Andriyash, William G. Macready
  NeurIPS'2018 [Paper] [Code]

• DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
  Arash Vahdat, William G. Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash
  ICML'2018 [Paper] [Code]

• Generating Diverse High-Fidelity Images with VQ-VAE-2
  Ali Razavi, Aaron van den Oord, Oriol Vinyals
  NeurIPS'2019 [Paper] [Code]

• Generative Pretraining from Pixels
  Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, David Luan, Ilya Sutskever
  ICML'2020 [Paper] [Code]

  iGPT Framework

  

• Taming Transformers for High-Resolution Image Synthesis
  Patrick Esser, Robin Rombach, Björn Ommer
  CVPR'2021 [Paper] [Code]

  VQGAN Framework

  

• MaskGIT: Masked Generative Image Transformer
  Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, William T. Freeman
  CVPR'2022 [Paper] [Code]

  MaskGIT Framework

  

• ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
  Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
  Arxiv'2021 [Paper] [Project]

• NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
  Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan
  Arxiv'2021 [Paper] [Code]

• ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
  Patrick Esser, Robin Rombach, Andreas Blattmann, Björn Ommer
  NeurIPS'2021 [Paper] [Code] [Project]

• Vector-quantized Image Modeling with Improved VQGAN
  Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, Yonghui Wu
  ICLR'2022 [Paper] [Code]

  ViT-VQGAN Framework

  

• Self-supervision through Random Segments with Autoregressive Coding (RandSAC)
  Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal
  ICLR'2023 [Paper]

  RandSAC Framework

  

• MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis
  Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan
  CVPR'2023 [Paper] [Code]

  MAGE Framework

  

• Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation
  Mengqi Huang, Zhendong Mao, Quan Wang, Yongdong Zhang
  CVPR'2023 [Paper] [Code]

  MQ-VAE Framework

  

• Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization
  Mengqi Huang, Zhendong Mao, Zhuowei Chen, Yongdong Zhang
  CVPR'2023 [Paper] [Code]

  DQ-VAE Framework

  

• Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
  Hao Liu, Wilson Yan, Pieter Abbeel
  ArXiv'2023 [Paper] [Code]

  LQAE Framework

  

• SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
  Lijun Yu, Yong Cheng, Zhiruo Wang, Vivek Kumar, Wolfgang Macherey, Yanping Huang, David A. Ross, Irfan Essa, Yonatan Bisk, Ming-Hsuan Yang, Kevin Murphy, Alexander G. Hauptmann, Lu Jiang
  ArXiv'2023 [Paper] [Code]

  SPAE Framework

  

• Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
  Jaewoong Lee, Sangwon Jang, Jaehyeong Jo, Jaehong Yoon, Yunji Kim, Jin-Hwa Kim, Jung-Woo Ha, Sung Ju Hwang
  ICCV'2023 [Paper]

  TCTS Framework

  

• Diffusion Models as Masked Autoencoders
  Chen Wei, Karttikeya Mangalam, Po-Yao Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, Alan Yuille, Christoph Feichtenhofer
  ICCV'2023 [Paper] [Code]

  TCTS Framework

  

• Masked Diffusion Transformer is a Strong Image Synthesizer
  Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan
  ICCV'2023 [Paper] [Code]

  MDT Framework

  

• Self-conditioned Image Generation via Generating Representations
  Tianhong Li, Dina Katabi, Kaiming He
  ArXiv'2023 [Paper] [Code]

  RCG Framework

  

• OneLLM: One Framework to Align All Modalities with Language
  Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, Xiangyu Yue
  ArXiv'2023 [Paper] [Code]

  OneLLM Framework

  

• Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
  Lei Zhu, Fangyun Wei, Yanye Lu
  CVPR'2024 [Paper] [Code]

  V2L Framework

  

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MIM for CV Downstream Tasks

Object Detection and Segmentation

• Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection
  Yuxin Fang, Shusheng Yang, Shijie Wang, Yixiao Ge, Ying Shan, Xinggang Wang
  ICCV'2023 [Paper] [Code]

  MIMDet Framework

  

• SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
  Guoqiang Jin, Fan Yang, Mingshan Sun, Ruyi Zhao, Yakun Liu, Wei Li, Tianpeng Bao, Liwei Wu, Xingyu Zeng, Rui Zhao
  ArXiv'2022 [Paper]

  SeqCo-DETR Framework

  

• Integrally Pre-Trained Transformer Pyramid Networks
  Yunjie Tian, Lingxi Xie, Zhaozhi Wang, Longhui Wei, Xiaopeng Zhang, Jianbin Jiao, Yaowei Wang, Qi Tian, Qixiang Ye
  CVPR'2023 [Paper] [Code]

  iTPN Framework

  

• PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
  Anthony Chen, Kevin Zhang, Renrui Zhang, Zihan Wang, Yuheng Lu, Yandong Guo, Shanghang Zhang
  CVPR'2023 [Paper] [Code]

  PiMAE Framework

  

• Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
  Yuan Liu, Songyang Zhang, Jiacheng Chen, Zhaohui Yu, Kai Chen, Dahua Lin
  ICCV'2023 [Paper] [Code]

  imTED Framework

  

• Masked Retraining Teacher-student Framework for Domain Adaptive Object Detection
  Zijing Zhao, Sitong Wei, Qingchao Chen, Dehui Li, Yifan Yang, Yuxin Peng, Yang Liu
  ICCV'2023 [Paper] [Code]

  MRT Framework

  

• Object Recognition as Next Token Prediction
  Kaiyu Yue, Bor-Chun Chen, Jonas Geiping, Hengduo Li, Tom Goldstein, Ser-Nam Lim
  arXiv'2023 [Paper] [Code]

  imTED Framework

  

• EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
  Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra
  CVPR'2024 [Paper] [Code]

  EfficientSAM Framework

  

Video Rrepresentation

• VideoGPT: Video Generation using VQ-VAE and Transformers
  Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas
  arXiv'2021 [Paper] [Code]

  VideoGPT Framework

  

• VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
  Zhan Tong, Yibing Song, Jue Wang, Limin Wang
  NeurIPS'2022 [Paper] [Code]

  VideoMAE Framework

  

• Masked Autoencoders As Spatiotemporal Learners
  Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, Kaiming He
  NeurIPS'2022 [Paper] [Code]

  MAE Framework

  

• Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
  Yiran Wang, Zhiyu Pan, Xingyi Li, Zhiguo Cao, Ke Xian, Jianming Zhang
  ACMMM'2022 [Paper] [Code]

  FMNet Framework

  

• MaskViT: Masked Visual Pre-Training for Video Prediction
  Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Martín-Martín, Li Fei-Fei
  CVPR'2022 [Paper] [Code]

  MaskViT Framework

  

• BEVT: BERT Pretraining of Video Transformers
  Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Yu-Gang Jiang, Luowei Zhou, Lu Yuan
  CVPR'2022 [Paper] [Code]

  BEVT Framework

  

• MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval
  Yuying Ge, Yixiao Ge, Xihui Liu, Alex Jinpeng Wang, Jianping Wu, Ying Shan, Xiaohu Qie, Ping Luo
  ArXiv'2022 [Paper] [Code]

  MILES Framework

  

• MAR: Masked Autoencoders for Efficient Action Recognition
  Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Xiang Wang, Yuehuan Wang, Yiliang Lv, Changxin Gao, Nong Sang
  ArXiv'2022 [Paper]

  MAR Framework

  

• Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders
  Haosen Yang, Deng Huang, Bin Wen, Jiannan Wu, Hongxun Yao, Yi Jiang, Xiatian Zhu, Zehuan Yuan
  ArXiv'2022 [Paper] [Code]

  MotionMAE Framework

  

• It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training
  Yuxin Song, Min Yang, Wenhao Wu, Dongliang He, Fu Li, Jingdong Wang
  ArXiv'2022 [Paper]

  MAM2 Framework

  

• NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
  Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan
  ECCV'2022 [Paper] [Code]

  NUWA Framework

  

• MIMT: Masked Image Modeling Transformer for Video Compression
  Jinxi Xiang, Kuan Tian, Jun Zhang
  ICLR'2023 [Paper]

  MIMT Framework

  

• VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
  Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, Yu Qiao
  CVPR'2023 [Paper] [Code]

  VideoMAE.V2 Framework

  

• OmniMAE: Single Model Masked Pretraining on Images and Videos
  Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra
  CVPR'2023 [Paper] [Code]

  OmniMAE Framework

  

• Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning
  Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, Yu-Gang Jiang
  CVPR'2023 [Paper] [Code]

  MVD Framework

  

• DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
  Qiangqiang Wu, Tianyu Yang, Ziquan Liu, Baoyuan Wu, Ying Shan, Antoni B. Chan
  CVPR'2023 [Paper] [Code]

  DropMAE Framework

  

• AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
  Wele Gedara Chaminda Bandara, Naman Patel, Ali Gholami, Mehdi Nikkhah, Motilal Agrawal, Vishal M. Patel
  CVPR'2023 [Paper] [Code]

  AdaMAE Framework

  

• MAGVIT: Masked Generative Video Transformer
  Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G. Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, Lu Jiang
  CVPR'2023 [Paper] [Code]

  MAGVIT Framework

  

• CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
  Cheng-Ze Lu, Xiaojie Jin, Zhicheng Huang, Qibin Hou, Ming-Ming Cheng, Jiashi Feng
  arXiv'2023 [Paper]

  CMAE-V Framework

  

• Siamese Masked Autoencoders
  Agrim Gupta, Jiajun Wu, Jia Deng, Li Fei-Fei
  NeurIPS'2023 [Paper] [Code]

  SiamMAE Framework

  

• MGMAE: Motion Guided Masking for Video Masked Autoencoding
  Bingkun Huang, Zhiyu Zhao, Guozhen Zhang, Yu Qiao, Limin Wang
  ICCV'2023 [Paper] [Code]

  MGMAE Framework

  

• Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders
  Jie Cheng, Xiaodong Mei, Ming Liu
  ICCV'2023 [Paper] [Code]

  Forecast-MAE Framework

  

• Traj-MAE: Masked Autoencoders for Trajectory Prediction
  Hao Chen, Jiaze Wang, Kun Shao, Furui Liu, Jianye Hao, Chenyong Guan, Guangyong Chen, Pheng-Ann Heng
  ICCV'2023 [Paper]

  Traj-MAE Framework

  

• HumanMAC: Masked Motion Completion for Human Motion Prediction
  Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu
  ICCV'2023 [Paper] [Code]

  HumanMAC Framework

  

• SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
  Hong Yan, Yang Liu, Yushen Wei, Zhen Li, Guanbin Li, Liang Lin
  ICCV'2023 [Paper] [Code]

  SkeletonMAE Framework

  

• Masked Motion Predictors are Strong 3D Action Representation Learners
  Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu
  ICCV'2023 [Paper] [Code]

  MAMP Framework

  

• GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding
  Jihao Liu, Tai Wang, Boxiao Liu, Qihang Zhang, Yu Liu, Hongsheng Li
  ICCV'2023 [Paper] [Code]

  GeoMIM Framework

  

• Motion-Guided Masking for Spatiotemporal Representation Learning
  David Fan, Jue Wang, Shuai Liao, Yi Zhu, Vimal Bhat, Hector Santos-Villalobos, Rohith MV, Xinyu Li
  ICCV'2023 [Paper]

  MGM Framework

  

• ModelScope Text-to-Video Technical Report
  Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang
  ArXiv'2023 [Paper] [Code]

  ModelScopeT2V Framework

  

• NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
  Chenfei Wu, Jian Liang, Xiaowei Hu, Zhe Gan, Jianfeng Wang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan
  NeurIPS'2023 [Paper] [Code]

  NUWA-Infinity Framework

  

• NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
  Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Gong Ming, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan
  ACL'2023 [Paper] [Code]

  NUWA-XL Framework

  

• VDT: General-purpose Video Diffusion Transformers via Mask Modeling
  Haoyu Lu, Guoxing Yang, Nanyi Fei, Yuqi Huo, Zhiwu Lu, Ping Luo, Mingyu Ding
  ICLR'2024 [Paper] [Code]

  VDT Framework

  

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Knowledge Distillation and Few-shot Classification

• Generic-to-Specific Distillation of Masked Autoencoders
  Wei Huang, Zhiliang Peng, Li Dong, Furu Wei, Jianbin Jiao, Qixiang Ye
  CVPR'2023 [Paper] [Code]

  G2SD Framework

  

• Masked Autoencoders Enable Efficient Knowledge Distillers
  Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, Cihang Xie
  CVPR'2023 [Paper] [Code]

  DMAE Framework

  

• Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
  Yuzhong Chen, Zhenxiang Xiao, Lin Zhao, Lu Zhang, Haixing Dai, David Weizhong Liu, Zihao Wu, Changhe Li, Tuo Zhang, Changying Li, Dajiang Zhu, Tianming Liu, Xi Jiang
  ICLR'2023 [Paper]

  MG-ViT Framework

  

• Masked Autoencoders Are Stronger Knowledge Distillers
  Shanshan Lao, Guanglu Song, Boxiao Liu, Yu Liu, Yujiu Yang
  ICCV'2023 [Paper]

  MKD Framework

  

Efficient Fine-tuning

• Masked Images Are Counterfactual Samples for Robust Fine-tuning
  Yao Xiao, Ziyi Tang, Pengxu Wei, Cong Liu, Liang Lin
  CVPR'2023 [Paper] [Code]

  Robust Finetuning Framework

  

• Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
  Johannes Lehner, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer, Lukas Miklautz, Sepp Hochreiter
  arXiv'2023 [Paper] [Code]

  MAE-CT Framework

  

• Masked Autoencoders are Efficient Class Incremental Learners
  Jiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng
  ICCV'2023 [Paper] [Code]

  MAE-CIL Framework

  

Medical Image

• Self Pre-training with Masked Autoencoders for Medical Image Analysis
  Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna
  ArXiv'2022 [Paper]

• Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
  Yang Luo, Zhineng Chen, Xieping Gao
  ArXiv'2022 [Paper]

• Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
  Hao Quan, Xingyu Li, Weixing Chen, Qun Bai, Mingchen Zou, Ruijie Yang, Tingting Zheng, Ruiqun Qi, Xinghua Gao, Xiaoyu Cui
  ArXiv'2022 [Paper] [Code]

• FreMAE: Fourier Transform Meets Masked Autoencoders for Medical Image Segmentation
  Wenxuan Wang, Jing Wang, Chen Chen, Jianbo Jiao, Lichao Sun, Yuanxiu Cai, Shanshan Song, Jiangyun Li
  ArXiv'2023 [Paper]

• Masked Image Modeling Advances 3D Medical Image Analysis
  Zekai Chen, Devansh Agarwal, Kshitij Aggarwal, Wiem Safta, Samit Hirawat, Venkat Sethuraman, Mariann Micsinai Balan, Kevin Brown
  WACV'2023 [Paper] [Code]

• MRM: Masked Relation Modeling for Medical Image Pre-Training with Genetics
  Qiushi Yang, Wuyang Li, Baopu Li, Yixuan Yuan
  ICCV'2023 [Paper] [Code]

• FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders
  Soumen Basu, Mayuna Gupta, Chetan Madan, Pankaj Gupta, Chetan Arora
  CVPR'2024 [Paper] [Code]

Face Recognition

• FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
  Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Jiankang Deng, Xinchao Wang, Hakan Bilen, Yang You
  ArXiv'2022 [Paper] Code]

Scene Text Recognition (OCR)

• MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining
  Pengyuan Lyu, Chengquan Zhang, Shanshan Liu, Meina Qiao, Yangliu Xu, Liang Wu, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang
  ArXiv'2022 [Paper]

• DiT: Self-supervised Pre-training for Document Image Transformer
  Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei
  ACMMM'2022 [Paper] Code] [Code]

  DiT Framework

  

• DocMAE: Document Image Rectification via Self-supervised Representation Learning
  Shaokai Liu, Hao Feng, Wengang Zhou, Houqiang Li, Cong Liu, Feng Wu
  ICME'2023 [Paper]

Remote Sensing Image

• SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
  Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon
  NeurIPS'2022 [Paper]

• CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
  Dilxat Muhtar, Xueliang Zhang, Pengfeng Xiao, Zhenshi Li, Feng Gu
  TGRS'2023 [Paper] [Code]

• Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
  Colorado J Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, Trevor Darrell
  ICCV'2023 [Paper]

• On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data
  Rongjun Qin, Guixiang Zhang, Yang Tang
  ArXiv'2023 [Paper]

• Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing
  Hugo Chan-To-Hing, Bharadwaj Veeravalli
  ArXiv'2024 [Paper]

3D Representation Learning

• Pre-Training 3D Point Cloud Transformers with Masked Point Modeling
  Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, Jiwen Lu
  CVPR'2022 [Paper] [Code]

• Masked Autoencoders for Point Cloud Self-supervised Learning
  Yatian Pang, Wenxiao Wang, Francis E.H. Tay, Wei Liu, Yonghong Tian, Li Yuan
  ECCV'2022 [Paper] [Code]

• Masked Discrimination for Self-Supervised Learning on Point Clouds
  Haotian Liu, Mu Cai, Yong Jae Lee
  ECCV'2022 [Paper] [Code]

• MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis
  Yaqian Liang, Shanshan Zhao, Baosheng Yu, Jing Zhang, Fazhi He
  ECCV'2022 [Paper]

• Voxel-MAE: Masked Autoencoders for Pre-training Large-scale Point Clouds
  Chen Min, Xinli Xu, Dawei Zhao, Liang Xiao, Yiming Nie, Bin Dai
  ArXiv'2022 [Paper]

• Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
  Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, Hongsheng Li
  NeurIPS'2022 [Paper]

• Ponder: Point Cloud Pre-training via Neural Rendering
  Renrui Zhang, Ziyu Guo, Peng Gao, Rongyao Fang, Bin Zhao, Dong Wang, Yu Qiao, Hongsheng Li
  ArXiv'2022 [Paper] [Code]

• Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
  Renrui Zhang, Liuhui Wang, Yu Qiao, Peng Gao, Hongsheng Li
  CVPR'2023 [Paper] [Code]

• GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training
  Xiaoyu Tian, Haoxi Ran, Yue Wang, Hang Zhao
  CVPR'2023 [Paper] [Code]

• VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
  Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M. Alvarez, Sanja Fidler, Chen Feng, Anima Anandkumar
  CVPR'2023 [Paper] [Code]

• Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
  Runpei Dong, Zekun Qi, Linfeng Zhang, Junbo Zhang, Jianjian Sun, Zheng Ge, Li Yi, Kaisheng Ma
  ICLR'2023 [Paper] [Code]

• Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
  Zekun Qi, Runpei Dong, Guofan Fan, Zheng Ge, Xiangyu Zhang, Kaisheng Ma, Li Yi
  ICML'2023 [Paper] [Code]

• MGM: A meshfree geometric multilevel method for systems arising from elliptic equations on point cloud surfaces
  Grady B. Wright, Andrew M. Jones, Varun Shankar
  ICCV'2023 [Paper]

• PointGPT: Auto-regressively Generative Pre-training from Point Clouds
  Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu, Li Yuan, Yufeng Yue
  NeurIPS'2023 [Paper] [Code]

• MATE: Masked Autoencoders are Online 3D Test-Time Learners
  M. Jehanzeb Mirza, Inkyu Shin, Wei Lin, Andreas Schriebl, Kunyang Sun, Jaesung Choe, Horst Possegger, Mateusz Kozinski, In So Kweon, Kun-Jin Yoon, Horst Bischof
  ICCV'2023 [Paper] [Code]

• Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
  Zhiqiang Shen, Xiaoxiao Sheng, Hehe Fan, Longguang Wang, Yulan Guo, Qiong Liu, Hao Wen, Xi Zhou
  ICCV'2023 [Paper] [Code]

  MaST-Pre

  

• UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
  Honghui Yang, Sha Zhang, Di Huang, Xiaoyang Wu, Haoyi Zhu, Tong He, Shixiang Tang, Hengshuang Zhao, Qibo Qiu, Binbin Lin, Xiaofei He, Wanli Ouyang
  ICCV'2023 [Paper] [Code]

  UniPAD

  

• PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm
  Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang
  ArXiv'2023 [Paper] [Code]

• NeRF-MAE : Masked AutoEncoders for Self Supervised 3D representation Learning for Neural Radiance Fields
  Muhammad Zubair Irshad, Sergey Zakahrov, Vitor Guizilini, Adrien Gaidon, Zsolt Kira, Rares Ambrus
  ArXiv'2023 [Paper] [Code]

• General Point Model with Autoencoding and Autoregressive
  Zhe Li, Zhangyang Gao, Cheng Tan, Bocheng Ren, Laurence Tianruo Yang, Stan Z. Li
  CVPR'2024 [Paper] [Code]

Depth Estimation

• MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation
  Muhammad Osama Khan, Junbang Liang, Chun-Kai Wang, Shan Yang, Yu Lou
  ArXiv'2023 [Paper]
  UniPAD

  

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Audio and Speech

• wav2vec: Unsupervised Pre-training for Speech Recognition
  Steffen Schneider, Alexei Baevski, Ronan Collobert, Michael Auli
  ArXiv'2019 [Paper] [Code]

• vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
  Alexei Baevski, Steffen Schneider, Michael Auli
  ArXiv'2019 [Paper] [Code]

• wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
  Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
  NeurIPS'2020 [Paper] [Code]

• HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
  Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed
  TASLP'2021 [Paper] [Code]

• MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation
  Junkun Chen, Mingbo Ma, Renjie Zheng, Liang Huang
  ArXiv'2021 [Paper]

• MAE-AST: Masked Autoencoding Audio Spectrogram Transformer
  Alan Baade, Puyuan Peng, David Harwath
  ArXiv'2022 [Paper] [Code]

• Masked Spectrogram Prediction For Self-Supervised Audio Pre-Training
  Dading Chong, Helin Wang, Peilin Zhou, Qingcheng Zeng
  ArXiv'2022 [Paper] [Code]

• Masked Autoencoders that Listen
  Po-Yao Huang, Hu Xu, Juncheng Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, Christoph Feichtenhofer
  NeurIPS'2022 [Paper] [Code]

• Contrastive Audio-Visual Masked Autoencoder
  Yuan Gong, Andrew Rouditchenko, Alexander H. Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, James Glass
  ICLR'2023 [Paper]

• Audiovisual Masked Autoencoders
  Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab
  ICCV'2023 [Paper]

  Framework

  

• Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
  Sarthak Yadav, Sergios Theodoridis, Lars Kai Hansen, Zheng-Hua Tan
  ICLR'2024 [Paper]

• Masked Audio Generation using a Single Non-Autoregressive Transformer
  Alon Ziv, Itai Gat, Gael Le Lan, Tal Remez, Felix Kreuk, Jade Copet, Alexandre Défossez, Gabriel Synnaeve, Yossi Adi
  ICLR'2024 [Paper] [Code]

AI for Science

Protein

• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
  Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
  PNAS'2020 [Paper] [Code]

• Transformer protein language models are unsupervised structure learners
  Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, Alexander Rives
  bioRxiv'2020 [Paper] [Code]

• Language models enable zero-shot prediction of the effects of mutations on protein function
  Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, Alexander Rives
  bioRxiv'2021 [Paper] [Code]

• Learning inverse folding from millions of predicted structures
  Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, Alexander Rives
  ICML'2022 [Paper] [Code]

• Evolutionary-scale prediction of atomic level protein structure with a language model
  Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives
  bioRxiv'2022 [Paper] [Code]

• ProteinBERT: A universal deep-learning model of protein sequence and function
  Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives
  Bioinformatics'2022 [Paper] [Code]

• Foldseek: fast and accurate protein structure search
  Michel van Kempen, Stephanie S. Kim, Charlotte Tumescheit, Milot Mirdita, Johannes Söding, Martin Steinegger
  Nature'2023 [Paper] [Code]

• SaProt: Protein Language Modeling with Structure-aware Vocabulary
  Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, Fajie Yuan
  ICLR'2024 [Paper] [Code]

• MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
  Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V Chawla, Stan Z. Li
  ICLR'2024 [Paper] [Code]

Chemistry

• Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules
  Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z. Li
  ICLR'2023 [Paper] [Code]

Physics

• W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting
  Xin Man, Chenghong Zhang, Jin Feng, Changyu Li, Jie Shao
  arXiv'2023 [Paper] [Code]

• Masked Autoencoders are PDE Learners
  Anthony Zhou, Amir Barati Farimani
  arXiv'2024 [Paper]

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Neuroscience Learning

• neuro2vec: Masked Fourier Spectrum Prediction for Neurophysiological Representation Learning
  Di Wu, Siyuan Li, Jie Yang, Mohamad Sawan
  arXiv'2022 [Paper] [Code]

  neuro2vec

  

• Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
  Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu
  ICLR'2024 [Paper]

  LaBraM

  

• Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
  Antonis Antoniades, Yiyi Yu, Joseph Canzano, William Wang, Spencer LaVere Smith
  ICLR'2024 [Paper] [Code]

  Neuroformer

  

Reinforcement Learning

• Mask-based Latent Reconstruction for Reinforcement Learning
  Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu, Zhibo Chen
  ArXiv'2022 [Paper]

• Masked Contrastive Representation Learning for Reinforcement Learning
  Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, Houqiang Li
  TPAMI'2023 [Paper] [Code]

• SMART: Self-supervised Multi-task pretrAining with contRol Transformers
  Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, Ashish Kapoor
  ICLR'2023 [Paper]

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Tabular Data

• ReMasker: Imputing Tabular Data with Masked Autoencoding
  Tianyu Du, Luca Melis, Ting Wang
  ICLR'2024 [Paper]

• MCM: Masked Cell Modeling for Anomaly Detection in Tabular Data
  Jiaxin Yin, Yuanyuan Qiao, Zitang Zhou, Xiangchao Wang, Jie Yang
  ICLR'2024 [Paper]

Analysis and Understanding of Masked Modeling

• Demystifying Self-Supervised Learning: An Information-Theoretical Framework
  Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency
  ICLR'2021 [Paper]

• A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
  Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora
  ICLR'2021 [Paper]

• Predicting What You Already Know Helps: Provable Self-Supervised Learning
  Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo
  NeurIPS'2021 [Paper]

• How to Understand Masked Autoencoders
  Shuhao Cao, Peng Xu, David A. Clifton
  ArXiv'2022 [Paper]

• Masked prediction tasks: a parameter identifiability view
  Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski
  ArXiv'2022 [Paper]

• Revealing the Dark Secrets of Masked Image Modeling
  Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang, Han Hu, Yue Cao
  ArXiv'2022 [Paper]

• Architecture-Agnostic Masked Image Modeling - From ViT back to CNN
  Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Kai Wang, Lei Shang, Baigui Sun, Hao Li, Stan.Z.Li
  ArXiv'2022 [Paper]

• On Data Scaling in Masked Image Modeling
  Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Yixuan Wei, Qi Dai, Han Hu
  CVPR'2023 [Paper]

• Towards Understanding Why Mask-Reconstruction Pretraining Helps in Downstream Tasks
  Jiachun Pan, Pan Zhou, Shuicheng Yan
  ArXiv'2022 [Paper]

• An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
  Gokul Karthik Kumar, Sahal Shaji Mullappilly, Abhishek Singh Gehlot
  ArXiv'2022 [Paper] [Code]

• Understanding Masked Image Modeling via Learning Occlusion Invariant Feature
  Xiangwen Kong, Xiangyu Zhang
  ArXiv'2022 [Paper] [Code]

• How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
  Qi Zhang, Yifei Wang, Yisen Wang
  NeurIPS'2022 [Paper] [Code]

• i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable
  Kevin Zhang, Zhiqiang Shen
  ArXiv'2022 [Paper]

• Understanding Masked Autoencoders via Hierarchical Latent Variable Models
  Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang
  CVPR'2023 [Paper] [Code]

• Evaluating Self-Supervised Learning via Risk Decomposition
  Yann Dubois, Tatsunori Hashimoto, Percy Liang
  ICML'2023 [Paper] [Code]

• Regeneration Learning: A Learning Paradigm for Data Generation
  Xu Tan, Tao Qin, Jiang Bian, Tie-Yan Liu, Yoshua Bengio
  ArXiv'2023 [Paper]

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Survey

• A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond
  Chaoning Zhang, Chenshuang Zhang, Junha Song, John Seon Keun Yi, Kang Zhang, In So Kweon
  IJCAI'2023 [Paper]

• Masked Autoencoders in Computer Vision: A Comprehensive Survey
  Zexian Zhou, Xiaojing Liu
  IEEE Access'2023 [Paper]

• Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
  Siyuan Li, Luyuan Zhang, Zedong Wang, Di Wu, Lirong Wu, Zicheng Liu, Jun Xia, Cheng Tan, Yang Liu, Baigui Sun, Stan Z. Li
  ArXiv'2023 [Paper] [Code]

Contribution

Feel free to send pull requests to add more links with the following Markdown format. Note that the abbreviation, the code link, and the figure link are optional attributes.

* **TITLE**<br>
*AUTHER*<br>
PUBLISH'YEAR [[Paper](link)] [[Code](link)]
   <details close>
   <summary>ABBREVIATION Framework</summary>
   <p align="center"><img width="90%" src="link_to_image" /></p>
   </details>

The main maintainer is Siyuan Li (@Lupin1998). We thank all contributors for Awesome-MIM, and current contributors include:

Related Project

Paper List of Masked Image Modeling

• Awesome-Masked-Autoencoders: A collection of literature after or concurrent with Masked Autoencoder (MAE).
• awesome-MIM: Reading list for research topics in Masked Image Modeling.
• Awesome-MIM: Awesome list of masked image modeling methods for self-supervised visual representation.
• awesome-self-supervised-learning: A curated list of awesome self-supervised methods.

Project of Self-supervised Learning

• unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities.
• OpenMixup: CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark.
• MMPretrain: OpenMMLab self-supervised pre-training toolbox and benchmark.
• solo-learn: A library of self-supervised methods for visual representation learning powered by Pytorch Lightning.
• VISSL: FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
• lightly: A python library for self-supervised learning on images.
• Fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

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