  Can we improve detection in the thermal domain by borrowing features from
rich domains like visual RGB? In this paper, we propose a pseudo-multimodal
object detector trained on natural image domain data to help improve the
performance of object detection in thermal images. We assume access to a
large-scale dataset in the visual RGB domain and relatively smaller dataset (in
terms of instances) in the thermal domain, as is common today. We propose the
use of well-known image-to-image translation frameworks to generate pseudo-RGB
equivalents of a given thermal image and then use a multi-modal architecture
for object detection in the thermal image. We show that our framework
outperforms existing benchmarks without the explicit need for paired training
examples from the two domains. We also show that our framework has the ability
to learn with less data from thermal domain when using our approach. Our code
and pre-trained models are made available at
https://github.com/tdchaitanya/MMTOD
