Papers
14 papers found
MCMAE: Masked Convolution Meets Masked Autoencoders
Peng Gao, Teli Ma, Hongsheng Li et al.
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Zhan Tong, Yibing Song, Jue Wang et al.
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
Gang Li, Heliang Zheng, Daqing Liu et al.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Renrui Zhang, Ziyu Guo, Peng Gao et al.
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Qi Zhang, Yifei Wang, Yisen Wang
Masked Autoencoders that Listen
Po-Yao Huang, Hu Xu, Juncheng Li et al.
Test-Time Training with Masked Autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen et al.
Masked Autoencoders As Spatiotemporal Learners
Christoph Feichtenhofer, haoqi fan, Yanghao Li et al.
Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie et al.
Masked Autoencoders for Point Cloud Self-Supervised Learning
Yatian Pang, Wenxiao Wang, Francis E.H. Tay et al.
MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis
Yaqian Liang, Shanshan Zhao, Baosheng Yu et al.
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
Xiaoyi Dong, Jianmin Bao, Ting Zhang et al.
Domain Invariant Masked Autoencoders for Self-Supervised Learning from Multi-Domains
Haiyang Yang, Shixiang Tang, Meilin Chen et al.
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Roman Bachmann, David Mizrahi, Andrei Atanov et al.