NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
Jerrick Liu
Nathan Inkawhich
Oliver A. Nina
Radu Timofte
Sahil Jain
Bob Lee
Yuru Duan
Wei Wei
Lei Zhang
Songzheng Xu
Yuxuan Sun
J. Tang
Xueli Geng
Mengru Ma
Gongzhe Li
Xueli Geng
Huanqia Cai
Chen Cai
Sol Cummings
Casian Miron
A. Pasarica
Cheng-Yen Yang
Hung-Min Hsu
Jiarui Cai
J. Mei
C. Yeh
Jenq-Neng Hwang
Michael Xin
Zhongkai Shangguan
Zihe Zheng
Xu Yifei
Lehan Yang
Kele Xu
Mingtao Feng
Abstract
In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO andSAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition
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