  In this work, we propose a novel topic consisting of two dual tasks: 1) given
a scene, recommend objects to insert, 2) given an object category, retrieve
suitable background scenes. A bounding box for the inserted object is predicted
in both tasks, which helps downstream applications such as semi-automated
advertising and video composition. The major challenge lies in the fact that
the target object is neither present nor localized at test time, whereas
available datasets only provide scenes with existing objects. To tackle this
problem, we build an unsupervised algorithm based on object-level contexts,
which explicitly models the joint probability distribution of object categories
and bounding boxes with a Gaussian mixture model. Experiments on our newly
annotated test set demonstrate that our system outperforms existing baselines
on all subtasks, and do so under a unified framework. Our contribution promises
future extensions and applications.
