  There has been significant progress in creating machine learning models that
identify objects in scenes along with their associated attributes and
relationships; however, there is a large gap between the best models and human
capabilities. One of the major reasons for this gap is the difficulty in
collecting sufficient amounts of annotated relations and attributes for
training these systems. While some attributes and relations are abundant, the
distribution in the natural world and existing datasets is long tailed. In this
paper, we address this problem by introducing a novel incremental active
learning framework that asks for attributes and relations in visual scenes.
While conventional active learning methods ask for labels of specific examples,
we flip this framing to allow agents to ask for examples from specific
categories. Using this framing, we introduce an active sampling method that
asks for examples from the tail of the data distribution and show that it
outperforms classical active learning methods on Visual Genome.
