  Supervised learning tends to produce more accurate classifiers than
unsupervised learning in general. This implies that training data is preferred
with annotations. When addressing visual perception challenges, such as
localizing certain object classes within an image, the learning of the involved
classifiers turns out to be a practical bottleneck. The reason is that, at
least, we have to frame object examples with bounding boxes in thousands of
images. A priori, the more complex the model is regarding its number of
parameters, the more annotated examples are required. This annotation task is
performed by human oracles, which ends up in inaccuracies and errors in the
annotations (aka ground truth) since the task is inherently very cumbersome and
sometimes ambiguous. As an alternative we have pioneered the use of virtual
worlds for collecting such annotations automatically and with high precision.
However, since the models learned with virtual data must operate in the real
world, we still need to perform domain adaptation (DA). In this chapter we
revisit the DA of a deformable part-based model (DPM) as an exemplifying case
of virtual- to-real-world DA. As a use case, we address the challenge of
vehicle detection for driver assistance, using different publicly available
virtual-world data. While doing so, we investigate questions such as: how does
the domain gap behave due to virtual-vs-real data with respect to dominant
object appearance per domain, as well as the role of photo-realism in the
virtual world.
