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Supervised learning tends to produce more accurate classifiers thanunsupervised learning in general. This implies that training data is preferredwith annotations. When addressing visual perception challenges, such aslocalizing certain object classes within an image, the learning of the involvedclassifiers turns out to be a practical bottleneck. The reason is that, atleast, we have to frame object examples with bounding boxes in thousands ofimages. A priori, the more complex the model is regarding its number ofparameters, the more annotated examples are required. This annotation task isperformed by human oracles, which ends up in inaccuracies and errors in theannotations (aka ground truth) since the task is inherently very cumbersome andsometimes ambiguous. As an alternative we have pioneered the use of virtualworlds for collecting such annotations automatically and with high precision.However, since the models learned with virtual data must operate in the realworld, we still need to perform domain adaptation (DA). In this chapter werevisit the DA of a deformable part-based model (DPM) as an exemplifying caseof virtual- to-real-world DA. As a use case, we address the challenge ofvehicle detection for driver assistance, using different publicly availablevirtual-world data. While doing so, we investigate questions such as: how doesthe domain gap behave due to virtual-vs-real data with respect to dominantobject appearance per domain, as well as the role of photo-realism in thevirtual world.

From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example