  Following recent breakthroughs in convolutional neural networks and
monolithic model architectures, state-of-the-art object detection models can
reliably and accurately scale into the realm of up to thousands of classes.
Things quickly break down, however, when scaling into the tens of thousands,
or, eventually, to millions or billions of unique objects. Further, bounding
box-trained end-to-end models require extensive training data. Even though -
with some tricks using hierarchies - one can sometimes scale up to thousands of
classes, the labor requirements for clean image annotations quickly get out of
control. In this paper, we present a two-layer object detection method for
brand logos and other stylized objects for which prototypical images exist. It
can scale to large numbers of unique classes. Our first layer is a CNN from the
Single Shot Multibox Detector family of models that learns to propose regions
where some stylized object is likely to appear. The contents of a proposed
bounding box is then run against an image index that is targeted for the
retrieval task at hand. The proposed architecture scales to a large number of
object classes, allows to continously add new classes without retraining, and
exhibits state-of-the-art quality on a stylized object detection task such as
logo recognition.
