Title: COSMO: Contextualized Scene Modeling with Boltzmann Machines,
Abstract: Scene modeling is very crucial for robots that need to perceive, reason about
and manipulate the objects in their environments. In this paper, we adapt and
extend Boltzmann Machines (BMs) for contextualized scene modeling. Although
there are many models on the subject, ours is the first to bring together
objects, relations, and affordances in a highly-capable generative model. For
this end, we introduce a hybrid version of BMs where relations and affordances
are introduced with shared, tri-way connections into the model. Moreover, we
contribute a dataset for relation estimation and modeling studies. We evaluate
our method in comparison with several baselines on object estimation,
out-of-context object detection, relation estimation, and affordance estimation
tasks. Moreover, to illustrate the generative capability of the model, we show
several example scenes that the model is able to generate.