  Providing ground truth supervision to train visual models has been a
bottleneck over the years, exacerbated by domain shifts which degenerate the
performance of such models. This was the case when visual tasks relied on
handcrafted features and shallow machine learning and, despite its
unprecedented performance gains, the problem remains open within the deep
learning paradigm due to its data-hungry nature. Best performing deep
vision-based object detectors are trained in a supervised manner by relying on
human-labeled bounding boxes which localize class instances (i.e.objects)
within the training images.Thus, object detection is one of such tasks for
which human labeling is a major bottleneck. In this paper, we assess
co-training as a semi-supervised learning method for self-labeling objects in
unlabeled images, so reducing the human-labeling effort for developing deep
object detectors. Our study pays special attention to a scenario involving
domain shift; in particular, when we have automatically generated virtual-world
images with object bounding boxes and we have real-world images which are
unlabeled. Moreover, we are particularly interested in using co-training for
deep object detection in the context of driver assistance systems and/or
self-driving vehicles. Thus, using well-established datasets and protocols for
object detection in these application contexts, we will show how co-training is
a paradigm worth to pursue for alleviating object labeling, working both alone
and together with task-agnostic domain adaptation.
