  In robot sensing scenarios, instead of passively utilizing human captured
views, an agent should be able to actively choose informative viewpoints of a
3D object as discriminative evidence to boost the recognition accuracy. This
task is referred to as active object recognition. Recent works on this task
rely on a massive amount of training examples to train an optimal view
selection policy. But in realistic robot sensing scenarios, the large-scale
training data may not exist and whether the intelligent view selection policy
can be still learned from few object samples remains unclear. In this paper, we
study this new problem which is extremely challenging but very meaningful in
robot sensing -- Few-shot Active Object Recognition, i.e., to learn view
selection policies from few object samples, which has not been considered and
addressed before. We solve the proposed problem by adopting the framework of
meta learning and name our method "MetaView". Extensive experiments on both
category-level and instance-level classification tasks demonstrate that the
proposed method can efficiently resolve issues that are hard for
state-of-the-art active object recognition methods to handle, and outperform
several baselines by large margins.
