NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

• URL: http://arxiv.org/abs/2107.01189v1
• Date: Fri, 2 Jul 2021 16:55:08 GMT
• Title: NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
• Authors: Jerrick Liu, Nathan Inkawhich, Oliver Nina, Radu Timofte, Sahil Jain, Bob Lee, Yuru Duan, Wei Wei, Lei Zhang, Songzheng Xu, Yuxuan Sun, Jiaqi Tang, Xueli Geng, Mengru Ma, Gongzhe Li, Xueli Geng, Huanqia Cai, Chengxue Cai, Sol Cummings, Casian Miron, Alexandru Pasarica, Cheng-Yen Yang, Hung-Min Hsu, Jiarui Cai, Jie Mei, Chia-Ying Yeh, Jenq-Neng Hwang, Michael Xin, Zhongkai Shangguan, Zihe Zheng, Xu Yifei, Lehan Yang, Kele Xu, Min Feng
• Abstract summary: 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 and SAR imagery. We discuss the top methods submitted for this competition and evaluate their results on our blind test set.
• Score: 88.89190054948325
• License: http://creativecommons.org/licenses/by/4.0/
• 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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