  This work addresses the unsupervised adaptation of an existing object
detector to a new target domain. We assume that a large number of unlabeled
videos from this domain are readily available. We automatically obtain labels
on the target data by using high-confidence detections from the existing
detector, augmented with hard (misclassified) examples acquired by exploiting
temporal cues using a tracker. These automatically-obtained labels are then
used for re-training the original model. A modified knowledge distillation loss
is proposed, and we investigate several ways of assigning soft-labels to the
training examples from the target domain. Our approach is empirically evaluated
on challenging face and pedestrian detection tasks: a face detector trained on
WIDER-Face, which consists of high-quality images crawled from the web, is
adapted to a large-scale surveillance data set; a pedestrian detector trained
on clear, daytime images from the BDD-100K driving data set is adapted to all
other scenarios such as rainy, foggy, night-time. Our results demonstrate the
usefulness of incorporating hard examples obtained from tracking, the advantage
of using soft-labels via distillation loss versus hard-labels, and show
promising performance as a simple method for unsupervised domain adaptation of
object detectors, with minimal dependence on hyper-parameters.
