  Much of the focus in the object detection literature has been on the problem
of identifying the bounding box of a particular class of object in an image.
Yet, in contexts such as robotics and augmented reality, it is often necessary
to find a specific object instance---a unique toy or a custom industrial part
for example---rather than a generic object class. Here, applications can
require a rapid shift from one object instance to another, thus requiring fast
turnaround which affords little-to-no training time. What is more, gathering a
dataset and training a model for every new object instance to be detected can
be an expensive and time-consuming process. In this context, we propose a
generic 2D object instance detection approach that uses example viewpoints of
the target object at test time to retrieve its 2D location in RGB images,
without requiring any additional training (i.e. fine-tuning) step. To this end,
we present an end-to-end architecture that extracts global and local
information of the object from its viewpoints. The global information is used
to tune early filters in the backbone while local viewpoints are correlated
with the input image. Our method offers an improvement of almost 30 mAP over
the previous template matching methods on the challenging Occluded Linemod
dataset (overall mAP of 50.7). Our experiments also show that our single
generic model (not trained on any of the test objects) yields detection results
that are on par with approaches that are trained specifically on the target
objects.
