  Digital camera pipelines employ color constancy methods to estimate an
unknown scene illuminant, in order to re-illuminate images as if they were
acquired under an achromatic light source. Fully-supervised learning approaches
exhibit state-of-the-art estimation accuracy with camera-specific labelled
training imagery. Resulting models typically suffer from domain gaps and fail
to generalise across imaging devices. In this work, we propose a new approach
that affords fast adaptation to previously unseen cameras, and robustness to
changes in capture device by leveraging annotated samples across different
cameras and datasets. We present a general approach that utilizes the concept
of color temperature to frame color constancy as a set of distinct, homogeneous
few-shot regression tasks, each associated with an intuitive physical meaning.
We integrate this novel formulation within a meta-learning framework, enabling
fast generalisation to previously unseen cameras using only handfuls of camera
specific training samples. Consequently, the time spent for data collection and
annotation substantially diminishes in practice whenever a new sensor is used.
To quantify this gain, we evaluate our pipeline on three publicly available
datasets comprising 12 different cameras and diverse scene content. Our
approach delivers competitive results both qualitatively and quantitatively
while requiring a small fraction of the camera-specific samples compared to
standard approaches.
