  In this study, a novel illuminant color estimation framework is proposed for
computational color constancy, which incorporates the high representational
capacity of deep-learning-based models and the great interpretability of
assumption-based models. The well-designed building block, feature map reweight
unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with
respect to prior state-of-the-art deep learning based models while requiring
more compact model size and cheaper computational cost. In addition to local
color estimation, a confidence estimation branch is also included such that the
model is able to simultaneously produce point estimate and its uncertainty
estimate, which provides useful clues for local estimates aggregation and
multiple illumination estimation. The source code and the dataset have been
made available at https://github.com/QiuJueqin/Reweight-CC.
