  The assumption of a uniform light color distribution is no longer applicable
in scenes that have multiple light colors. Most color constancy methods are
designed to deal with a single light color, and thus are erroneous when applied
to multiple light colors. The spatial variability in multiple light colors
causes the color constancy problem to be more challenging and requires the
extraction of local surface/light information. Motivated by this, we introduce
a multi-task learning method to discount multiple light colors in a single
input image. To have better cues of the local surface/light colors under
multiple light color conditions, we design a novel multi-task learning
framework. Our framework includes auxiliary tasks of achromatic-pixel detection
and surface-color similarity prediction, providing better cues for local light
and surface colors, respectively. Moreover, to ensure that our model maintains
the constancy of surface colors regardless of the variations of light colors, a
novel local surface color feature preservation scheme is developed. We
demonstrate that our model achieves 47.1% improvement (from 4.69 mean angular
error to 2.48) compared to a state-of-the-art multi-illuminant color constancy
method on a multi-illuminant dataset (LSMI).
