  In object recognition research, many commonly used datasets (e.g., ImageNet
and similar) contain relatively sparse distributions of object instances and
views, e.g., one might see a thousand different pictures of a thousand
different giraffes, mostly taken from a few conventionally photographed angles.
These distributional properties constrain the types of computational
experiments that are able to be conducted with such datasets, and also do not
reflect naturalistic patterns of embodied visual experience. As a contribution
to the small (but growing) number of multi-view object datasets that have been
created to bridge this gap, we introduce a new video dataset called Toybox that
contains egocentric (i.e., first-person perspective) videos of common household
objects and toys being manually manipulated to undergo structured
transformations, such as rotation, translation, and zooming. To illustrate
potential uses of Toybox, we also present initial neural network experiments
that examine 1) how training on different distributions of object instances and
views affects recognition performance, and 2) how viewpoint-dependent object
concepts are represented within the hidden layers of a trained network.
