Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning

30 December 2022

Papers citing "Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning"

5 / 5 papers shown

Title
PyViT-FUSE: A Foundation Model for Multi-Sensor Earth Observation Data Manuel Weber Carly Beneke ViT 34 8 0 26 Apr 2025
EarthNets: Empowering AI in Earth Observation Zhitong Xiong Fahong Zhang Yi Wang Yilei Shi Xiao Xiang Zhu 64 70 0 10 Oct 2022
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training Renrui Zhang Ziyu Guo Rongyao Fang Bingyan Zhao Dong Wang Yu Qiao Hongsheng Li Peng Gao 3DPC 142 171 0 28 May 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 233 5,353 0 11 Nov 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 257 4,299 0 29 Apr 2021