  In this paper we present a new approach for feature fusion between RGB and
LWIR Thermal images for the task of semantic segmentation for driving
perception. We propose DooDLeNet, a double DeepLab architecture with
specialized encoder-decoders for thermal and color modalities and a shared
decoder for final segmentation. We combine two strategies for feature fusion:
confidence weighting and correlation weighting. We report state-of-the-art mean
IoU results on the MF dataset.
