Recent object detection models for infrared (IR) imagery are based upon deep
neural networks (DNNs) and require large amounts of labeled training imagery.
However, publicly-available datasets that can be used for such training are
limited in their size and diversity. To address this problem, we explore
cross-modal style transfer (CMST) to leverage large and diverse color imagery
datasets so that they can be used to train DNN-based IR image based object
detectors. We evaluate six contemporary stylization methods on four
publicly-available IR datasets - the first comparison of its kind - and find
that CMST is highly effective for DNN-based detectors. Surprisingly, we find
that existing data-driven methods are outperformed by a simple grayscale
stylization (an average of the color channels). Our analysis reveals that
existing data-driven methods are either too simplistic or introduce significant
artifacts into the imagery. To overcome these limitations, we propose
meta-learning style transfer (MLST), which learns a stylization by composing
and tuning well-behaved analytic functions. We find that MLST leads to more
complex stylizations without introducing significant image artifacts and
achieves the best overall detector performance on our benchmark datasets