  Drawings are powerful means of pictorial abstraction and communication.
Understanding diverse forms of drawings, including digital arts, cartoons, and
comics, has been a major problem of interest for the computer vision and
computer graphics communities. Although there are large amounts of digitized
drawings from comic books and cartoons, they contain vast stylistic variations,
which necessitate expensive manual labeling for training domain-specific
recognizers. In this work, we show how self-supervised learning, based on a
teacher-student network with a modified student network update design, can be
used to build face and body detectors. Our setup allows exploiting large
amounts of unlabeled data from the target domain when labels are provided for
only a small subset of it. We further demonstrate that style transfer can be
incorporated into our learning pipeline to bootstrap detectors using a vast
amount of out-of-domain labeled images from natural images (i.e., images from
the real world). Our combined architecture yields detectors with
state-of-the-art (SOTA) and near-SOTA performance using minimal annotation
effort. Our code can be accessed from
https://github.com/barisbatuhan/DASS_Detector.
