  Modern image classifiers perform well on populated classes, while degrading
considerably on tail classes with only a few instances. Humans, by contrast,
effortlessly handle the long-tailed recognition challenge, since they can learn
the tail representation based on different levels of semantic abstraction,
making the learned tail features more discriminative. This phenomenon motivated
us to propose SuperDisco, an algorithm that discovers super-class
representations for long-tailed recognition using a graph model. We learn to
construct the super-class graph to guide the representation learning to deal
with long-tailed distributions. Through message passing on the super-class
graph, image representations are rectified and refined by attending to the most
relevant entities based on the semantic similarity among their super-classes.
Moreover, we propose to meta-learn the super-class graph under the supervision
of a prototype graph constructed from a small amount of imbalanced data. By
doing so, we obtain a more robust super-class graph that further improves the
long-tailed recognition performance. The consistent state-of-the-art
experiments on the long-tailed CIFAR-100, ImageNet, Places and iNaturalist
demonstrate the benefit of the discovered super-class graph for dealing with
long-tailed distributions.
