NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

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...

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Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: Liu, Jerrick, Inkawhich, Nathan, Oliver, Nina, Timofte, Radu, Jain, Sahil, Lee, Bob, Duan, Yuru, Wei, Wei, Zhang, Lei, Xu, Songzheng, Sun, Yuxuan, Tang, Jiaqi, Geng, Xueli, Ma, Mengru, Li, Gongzhe, Cai, Huanqia, Cai, Chengxue, Cummings, Sol, Casian Miron, Pasarica, Alexandru, Cheng-Yen, Yang, Hung-Min, Hsu, Cai, Jiarui, Mei, Jie, Yeh, Chia-Ying, Hwang, Jenq-Neng, Xin, Michael, Shangguan, Zhongkai, Zheng, Zihe, Xu, Yifei, Lehan Yang, Xu, Kele, Feng, Min
Format: Article
Language:English
Subjects:
Classification
Competition
Online Access:Get full text
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Summary: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
ISSN:2331-8422