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.
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- Awesome Masked Modeling for Self-supervised Vision Represention and Beyond
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Generative Pretraining from Pixels
Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, David Luan, Ilya Sutskever
ICML'2020 [Paper] [Code]iGPT Framework
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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
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BEiT: BERT Pre-Training of Image Transformers
Hangbo Bao, Li Dong, Furu Wei
ICLR'2022 [Paper] [Code]BEiT Framework
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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
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Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick
CVPR'2022 [Paper] [Code]MAE Framework
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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
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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
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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
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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
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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
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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
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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
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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
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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
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MultiMAE: Multi-modal Multi-task Masked Autoencoders
Roman Bachmann, David Mizrahi, Andrei Atanov, Amir Zamir
ECCV'2022 [Paper] [Code]MultiMAE Framework
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SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
Feng Liang, Yangguang Li, Diana Marculescu
ArXiv'2022 [Paper] [Code]SupMAE Framework
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MVP: Multimodality-guided Visual Pre-training
Longhui Wei, Lingxi Xie, Wengang Zhou, Houqiang Li, Qi Tian
ArXiv'2022 [Paper]MVP Framework
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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
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ConvMAE: Masked Convolution Meets Masked Autoencoders
Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, Yu Qiao
NeurIPS'2022 [Paper] [Code]ConvMAE Framework
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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
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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
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Test-Time Training with Masked Autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros
NeurIPS'2022 [Paper] [Code]TTT-MAE Framework
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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
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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
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Object-wise Masked Autoencoders for Fast Pre-training
Jiantao Wu, Shentong Mo
ArXiv'2022 [Paper]ObjMAE Framework
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Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction
Jun Chen, Ming Hu, Boyang Li, Mohamed Elhoseiny
ArXiv'2022 [Paper] [Code]LoMaR Framework
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Extreme Masking for Learning Instance and Distributed Visual Representations
Zhirong Wu, Zihang Lai, Xiao Sun, Stephen Lin
ArXiv'2022 [Paper]ExtreMA Framework
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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
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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
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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
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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
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A Unified View of Masked Image Modeling
Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, Furu Wei
ArXiv'2022 [Paper] [Code]MaskDistill Framework
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DILEMMA: Self-Supervised Shape and Texture Learning with Transformers
Sepehr Sameni, Simon Jenni, Paolo Favaro
AAAI'2023 [Paper]DILEMMA Framework
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i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable
Kevin Zhang, Zhiqiang Shen
ArXiv'2022 [Paper] [Code]i-MAE Framework
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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
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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
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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
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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
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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
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Exploring Target Representations for Masked Autoencoders
Xingbin Liu, Jinghao Zhou, Tao Kong, Xianming Lin, Rongrong Ji
ICLR'2024 [Paper] [Code]dBOT Framework
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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
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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
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TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Sucheng Ren, Fangyun Wei, Zheng Zhang, Han Hu
ArXiv'2023 [Paper] [Code]TinyMIM Framework
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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
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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
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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
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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
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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
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MOMA: Distill from Self-Supervised Teachers
Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
arXiv'2023 [Paper]MOMA Framework
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PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin
arXiv'2023 [Paper] [Code]PixMIM Framework
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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DeepMIM: Deep Supervision for Masked Image Modeling
Sucheng Ren, Fangyun Wei, Samuel Albanie, Zheng Zhang, Han Hu
arXiv'2023 [Paper] [Code]RobustMAE Framework
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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
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Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Xinlei Chen, Zhuang Liu, Saining Xie, Kaiming He
ArXiv'2024 [Paper]l-DAE Framework
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Denoising Autoregressive Representation Learning
Yazhe Li, Jorg Bornschein, Ting Chen
ArXiv'2024 [Paper]DARL Framework
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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
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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
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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
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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
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Masked Contrastive Representation Learning
Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
ArXiv'2022 [Paper]MACRL Framework
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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
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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
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Masked Siamese ConvNets
Li Jing, Jiachen Zhu, Yann LeCun
ArXiv'2022 [Paper]MSCN Framework
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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
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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
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MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
Qiang Zhou, Chaohui Yu, Hao Luo, Zhibin Wang, Hao Li
ArXiv'2022 [Paper]MimCo Framework
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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
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How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Qi Zhang, Yifei Wang, Yisen Wang
NeurIPS'2022 [Paper] [Code]U-MAE Framework
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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
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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
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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
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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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Context Encoders: Feature Learning by Inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros
CVPR'2016 [Paper] [Code]Context-Encoder Framework
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Corrupted Image Modeling for Self-Supervised Visual Pre-Training
Yuxin Fang, Li Dong, Hangbo Bao, Xinggang Wang, Furu Wei
ICLR'2023 [Paper]CIM Framework
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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
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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
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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
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Masked Autoencoders are Robust Data Augmentors
Haohang Xu, Shuangrui Ding, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
ArXiv'2022 [Paper] [Code]MRA Framework
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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
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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
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RevColV2: Exploring Disentangled Representations in Masked Image Modeling
Qi Han, Yuxuan Cai, Xiangyu Zhang
NeurIPS'2023 [Paper] [Code]RevCol.V2 Framework
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Masked Capsule Autoencoders
Miles Everett, Mingjun Zhong, Georgios Leontidis
arXiv'2024 [Paper]MCAE Framework
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MixMask: Revisiting Masking Strategy for Siamese ConvNets
Kirill Vishniakov, Eric Xing, Zhiqiang Shen
BMVC'2024 [Paper] [Code]MixMask Framework
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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
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Adversarial Masking for Self-Supervised Learning
Yuge Shi, N. Siddharth, Philip H.S. Torr, Adam R. Kosiorek
ICML'2022 [Paper] [Code]ADIOS Framework
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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
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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
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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
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Good helper is around you: Attention-driven Masked Image Modeling
Zhengqi Liu, Jie Gui, Hao Luo
AAAI'2023 [Paper] [Code]AMT Framework
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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
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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
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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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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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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
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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
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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
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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
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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
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Scaling Language-Image Pre-training via Masking
Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, Kaiming He
CVPR'2023 [Paper]FLIP Framework
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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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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
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Taming Transformers for High-Resolution Image Synthesis
Patrick Esser, Robin Rombach, Björn Ommer
CVPR'2021 [Paper] [Code]VQGAN Framework
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MaskGIT: Masked Generative Image Transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, William T. Freeman
CVPR'2022 [Paper] [Code]MaskGIT Framework
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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
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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
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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
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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
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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
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Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
Hao Liu, Wilson Yan, Pieter Abbeel
ArXiv'2023 [Paper] [Code]LQAE Framework
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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
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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
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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
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Masked Diffusion Transformer is a Strong Image Synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan
ICCV'2023 [Paper] [Code]MDT Framework
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Self-conditioned Image Generation via Generating Representations
Tianhong Li, Dina Katabi, Kaiming He
ArXiv'2023 [Paper] [Code]RCG Framework
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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
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MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan
ArXiv'2024 [Paper] [Code]MDTv2 Framework
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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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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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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
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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
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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
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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
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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
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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
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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
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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
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VideoGPT: Video Generation using VQ-VAE and Transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas
arXiv'2021 [Paper] [Code]VideoGPT Framework
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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
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Masked Autoencoders As Spatiotemporal Learners
Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, Kaiming He
NeurIPS'2022 [Paper] [Code]MAE Framework
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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
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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
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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
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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
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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
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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
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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
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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
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MIMT: Masked Image Modeling Transformer for Video Compression
Jinxi Xiang, Kuan Tian, Jun Zhang
ICLR'2023 [Paper]MIMT Framework
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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
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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
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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
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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
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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
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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
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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
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Siamese Masked Autoencoders
Agrim Gupta, Jiajun Wu, Jia Deng, Li Fei-Fei
NeurIPS'2023 [Paper] [Code]SiamMAE Framework
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MGMAE: Motion Guided Masking for Video Masked Autoencoding
Bingkun Huang, Zhiyu Zhao, Guozhen Zhang, Yu Qiao, Limin Wang
ICCV'2023 [Paper] [Code]MGMAE Framework
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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
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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
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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
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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
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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
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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
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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
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ModelScope Text-to-Video Technical Report
Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang
ArXiv'2023 [Paper] [Code]ModelScopeT2V Framework
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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
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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
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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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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
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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
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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
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Masked Autoencoders Are Stronger Knowledge Distillers
Shanshan Lao, Guanglu Song, Boxiao Liu, Yu Liu, Yujiu Yang
ICCV'2023 [Paper]MKD Framework
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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
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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
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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
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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
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Pseudo Labelling for Enhanced Masked Autoencoders
Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng, Josef Kittler, Muhammad Awais
arXiv'2024 [Paper]SdAE Framework
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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]
- 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]
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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
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DocMAE: Document Image Rectification via Self-supervised Representation Learning
Shaokai Liu, Hao Feng, Wengang Zhou, Houqiang Li, Cong Liu, Feng Wu
ICME'2023 [Paper]
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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]
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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
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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
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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]
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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]
- 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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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
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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]
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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]
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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]
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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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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)
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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
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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
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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]
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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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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]
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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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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]
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:
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},
}
- 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.
- 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.