  Computational color constancy is a preprocessing step used in many camera
systems. The main aim is to discount the effect of the illumination on the
colors in the scene and restore the original colors of the objects. Recently,
several deep learning-based approaches have been proposed to solve this problem
and they often led to state-of-the-art performance in terms of average errors.
However, for extreme samples, these methods fail and lead to high errors. In
this paper, we address this limitation by proposing to aggregate different deep
learning methods according to their output uncertainty. We estimate the
relative uncertainty of each approach using Monte Carlo dropout and the final
illumination estimate is obtained as the sum of the different model estimates
weighted by the log-inverse of their corresponding uncertainties. The proposed
framework leads to state-of-the-art performance on INTEL-TAU dataset.
