  The development of advanced 3D sensors has enabled many objects to be
captured in the wild at a large scale, and a 3D object recognition system may
therefore encounter many objects for which the system has received no training.
Zero-Shot Learning (ZSL) approaches can assist such systems in recognizing
previously unseen objects. Applying ZSL to 3D point cloud objects is an
emerging topic in the area of 3D vision, however, a significant problem that
ZSL often suffers from is the so-called hubness problem, which is when a model
is biased to predict only a few particular labels for most of the test
instances. We observe that this hubness problem is even more severe for 3D
recognition than for 2D recognition. One reason for this is that in 2D one can
use pre-trained networks trained on large datasets like ImageNet, which
produces high-quality features. However, in the 3D case there are no such
large-scale, labelled datasets available for pre-training which means that the
extracted 3D features are of poorer quality which, in turn, exacerbates the
hubness problem. In this paper, we therefore propose a loss to specifically
address the hubness problem. Our proposed method is effective for both
Zero-Shot and Generalized Zero-Shot Learning, and we perform extensive
evaluations on the challenging datasets ModelNet40, ModelNet10, McGill and
SHREC2015. A new state-of-the-art result for both zero-shot tasks in the 3D
case is established.
