A powerful way to adapt a visual recognition model to a new domain is through
image translation. However, common image translation approaches only focus on
generating data from the same distribution of the target domain. In visual
recognition tasks with complex images, such as pedestrian detection on aerial
images with a large cross-modal shift in data distribution from Infrared (IR)
to RGB images, a translation focused on generation might lead to poor
performance as the loss focuses on irrelevant details for the task. In this
paper, we propose HalluciDet, an IR-RGB image translation model for object
detection that, instead of focusing on reconstructing the original image on the
IR modality, is guided directly on reducing the detection loss of an RGB
detector, and therefore avoids the need to access RGB data. This model produces
a new image representation that enhances the object of interest in the scene
and greatly improves detection performance. We empirically compare our approach
against state-of-the-art image translation methods as well as with the commonly
used fine-tuning on IR, and show that our method improves detection accuracy in
most cases, by exploiting the privileged information encoded in a pre-trained
RGB detector.

通过提出 HalluciDet，一种 IR-RGB 图像翻译模型，将重点放在对象检测的准确性上，而不是重构 IR 模态下的原始图像，从而绕过了访问 RGB 数据的需求，从而提高了检测性能。