NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
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
TL;DR
This paper presents the NTIRE 2021 MAVOC challenge, focusing on multi-modal aerial object classification using EO and SAR imagery, highlighting the effectiveness of combining sensory data for improved accuracy.
Contribution
It introduces the first multi-modal aerial classification challenge, providing a platform to evaluate methods combining EO and SAR data for enhanced object recognition.
Findings
Over 15% accuracy improvement over baselines
Top methods effectively leverage multi-modal data
Challenge results demonstrate the benefit of multi-sensor fusion
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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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Infrared Target Detection Methodologies