We propose a neural network-based solution for three different tracks of 2nd International Illumination Estimation Challenge (chromaticity.iitp.ru). Our method is built on pre-trained Squeeze-Net backbone, differential 2D chroma histogram layer and a shallow MLP utilizing Exif information. By combining semantic feature, color feature and Exif metadata, the resulting method -- SDE-AWB -- obtains 1st place in both indoor and two-illuminant tracks and 2nd place in general track.
@article{arxiv.2010.05149,
title = {SDE-AWB: a Generic Solution for 2nd International Illumination Estimation Challenge},
author = {Yanlin Qian and Sibo Feng and Kang Qian and Miaofeng Wang},
journal= {arXiv preprint arXiv:2010.05149},
year = {2020}
}
Comments
6 pages; challenge paper for 2nd International Illumination Estimation Challenge, held in International Conference on Machine Vision 2020