  Measuring concept generalization, i.e., the extent to which models trained on
a set of (seen) visual concepts can be used to recognize a new set of (unseen)
concepts, is a popular way of evaluating visual representations, especially
when they are learned with self-supervised learning. Nonetheless, the choice of
which unseen concepts to use is usually made arbitrarily, and independently
from the seen concepts used to train representations, thus ignoring any
semantic relationships between the two. In this paper, we argue that semantic
relationships between seen and unseen concepts affect generalization
performance and propose ImageNet-CoG, a novel benchmark on the ImageNet dataset
that enables measuring concept generalization in a principled way. Our
benchmark leverages expert knowledge that comes from WordNet in order to define
a sequence of unseen ImageNet concept sets that are semantically more and more
distant from the ImageNet-1K subset, a ubiquitous training set. This allows us
to benchmark visual representations learned on ImageNet-1K out-of-the box: we
analyse a number of such models from supervised, semi-supervised and
self-supervised approaches under the prism of concept generalization, and show
how our benchmark is able to uncover a number of interesting insights. We will
provide resources for the benchmark at
https://europe.naverlabs.com/cog-benchmark.
