  Color constancy aims to restore the constant colors of a scene under
different illuminants. However, due to the existence of camera spectral
sensitivity, the network trained on a certain sensor, cannot work well on
others. Also, since the training datasets are collected in certain
environments, the diversity of illuminants is limited for complex real world
prediction. In this paper, we tackle these problems via two aspects. First, we
propose cross-sensor self-supervised training to train the network. In detail,
we consider both the general sRGB images and the white-balanced RAW images from
current available datasets as the white-balanced agents. Then, we train the
network by randomly sampling the artificial illuminants in a sensor-independent
manner for scene relighting and supervision. Second, we analyze a previous
cascaded framework and present a more compact and accurate model by sharing the
backbone parameters with learning attention specifically. Experiments show that
our cross-sensor model and single-sensor model outperform other
state-of-the-art methods by a large margin on cross and single sensor
evaluations, respectively, with only 16% parameters of the previous best model.
