NTIRE 2021 Multi-Modal Aerial View Object Classification Challenge

Jerrick Liu, Nathan Inkawhich, Oliver Nina, Radu Timofte

CVPRW 2021 pp. 588-595

doi:10.1109/CVPRW53098.2021.00071 /cvprw/2021/liu2021cvprw-ntire/

Abstract

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in con-junction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR 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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Cite

Text

Liu et al. "NTIRE 2021 Multi-Modal Aerial View Object Classification Challenge." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00071

Markdown

[Liu et al. "NTIRE 2021 Multi-Modal Aerial View Object Classification Challenge." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/liu2021cvprw-ntire/) doi:10.1109/CVPRW53098.2021.00071

BibTeX

@inproceedings{liu2021cvprw-ntire,
  title     = {{NTIRE 2021 Multi-Modal Aerial View Object Classification Challenge}},
  author    = {Liu, Jerrick and Inkawhich, Nathan and Nina, Oliver and Timofte, Radu},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2021},
  pages     = {588-595},
  doi       = {10.1109/CVPRW53098.2021.00071},
  url       = {https://mlanthology.org/cvprw/2021/liu2021cvprw-ntire/}
}