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Awesome Masked Modeling for Self-supervised Vision Represention and Beyond

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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

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
    ICLR'2024 [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

  • BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling
    Yixuan Luo, Mengye Ren, Sai Qian Zhang
    ArXiv'2023 [Paper]

  • 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

  • MixMask: Revisiting Masking Strategy for Siamese ConvNets
    Kirill Vishniakov, Eric Xing, Zhiqiang Shen
    BMVC'2024 [Paper] [Code]

    MixMask 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

  • Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
    Yuanpeng Tu, Boshen Zhang, Yuxi Li, Liang Liu, Jian Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Cai Rong Zhao
    arXiv'2023 [Paper] [Code]

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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

  • Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
    Johnathan Wenjia Xie, Yoonho Lee, Annie S Chen, Chelsea Finn
    ICLR'2024 [Paper] [Code]

    SMA Framework

MIM for Vision Generalist Model

  • A Generalist Agent
    Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas
    TMLR'2022 [Paper] [Code]

    Gato Framework

  • Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
    Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, Yonghui Wu
    TMLR'2022 [Paper]

    Parti Framework

  • 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

  • MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer
    Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan
    ArXiv'2024 [Paper] [Code]

    MDTv2 Framework

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

    V2L Framework

  • Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
    Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, Zehuan Yuan
    ArXiv'2024 [Paper] [Code]

  • Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%
    Lei Zhu, Fangyun Wei, Yanye Lu, Dong Chen
    ArXiv'2024 [Paper] [Code]

    VQGAN-LC 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

  • MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning
    Wenjin Zhang, Keyi Li, Sen Yang, Chenyang Gao, Wanzhao Yang, Sifan Yuan, Ivan Marsic
    arXiv'2024 [Paper]

    MaskMatch Framework

  • Pseudo Labelling for Enhanced Masked Autoencoders
    Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng, Josef Kittler, Muhammad Awais
    arXiv'2024 [Paper]

    SdAE 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]

  • SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification
    Junyan Lin, Feng Gao, Xiaocheng Shi, Junyu Dong, Qian Du
    ArXiv'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]

  • S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data
    Xuyang Li, Danfeng Hong, Jocelyn Chanussot
    CVPR'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]

Low-level Vision

  • DegAE: A New Pretraining Paradigm for Low-level Vision
    Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, Chao Dong
    CVPR'2023 [Paper] [Code]

  • LM4LV: A Frozen Large Language Model for Low-level Vision Tasks
    Boyang Zheng, Jinjin Gu, Shijun Li, Chao Dong
    ArXiv'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]

  • VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
    Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li
    ICML'2024 [Paper]

  • Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
    Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V Chawla, Stan Z. Li
    ICML'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]

  • VQMAE: Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces
    Fang Wu, Stan Z. Li
    ICML'2024 [Paper]

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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Time Series and Neuroscience Learning

  • Neuro-BERT: Rethinking Masked Autoencoding for Self-Supervised Neurological Pretraining
    Di Wu, Siyuan Li, Jie Yang, Mohamad Sawan
    JBHI'2024 [Paper] [Code]

    Neuro-BERT (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

  • VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
    Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun, Chenghao Liu
    ArXiv'2024 [Paper] [Code]

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:

Citation

If you find this repository and our survey helpful, please consider citing our paper:

@article{Li2023MIMSurvey,
  title={Masked Modeling for Self-supervised Representation Learning on Vision and Beyond},
  author={Siyuan Li and Luyuan Zhang and Zedong Wang and Di Wu and Lirong Wu and Zicheng Liu and Jun Xia and Cheng Tan and Yang Liu and Baigui Sun and Stan Z. Li},
  journal={ArXiv},
  year={2023},
  volume={abs/2401.00897},
}

Related Project

Paper List of Masked Image Modeling

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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