Revisiting pre-trained remote sensing model benchmarks: resizing and
normalization matters

Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters

22 May 2023

Peyman Najafirad

Papers citing "Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters"

6 / 6 papers shown

Title
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries Gabriel Tseng Ruben Cartuyvels Ivan Zvonkov Mirali Purohit David Rolnick Hannah Kerner 45 42 0 27 Apr 2023
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning Colorado Reed Ritwik Gupta Shufan Li S. Brockman Christopher Funk Brian Clipp Kurt Keutzer Salvatore Candido M. Uyttendaele Trevor Darrell 89 93 0 30 Dec 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 255 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 280 4,299 0 29 Apr 2021
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery Esther Rolf J. Proctor T. Carleton I. Bolliger Vaishaal Shankar Miyabi Ishihara Benjamin Recht S. Hsiang 71 79 0 16 Oct 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 227 3,029 0 09 Mar 2020