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Modern image classifiers perform well on populated classes, while degradingconsiderably on tail classes with only a few instances. Humans, by contrast,effortlessly handle the long-tailed recognition challenge, since they can learnthe tail representation based on different levels of semantic abstraction,making the learned tail features more discriminative. This phenomenon motivatedus to propose SuperDisco, an algorithm that discovers super-classrepresentations for long-tailed recognition using a graph model. We learn toconstruct the super-class graph to guide the representation learning to dealwith long-tailed distributions. Through message passing on the super-classgraph, image representations are rectified and refined by attending to the mostrelevant entities based on the semantic similarity among their super-classes.Moreover, we propose to meta-learn the super-class graph under the supervisionof a prototype graph constructed from a small amount of imbalanced data. Bydoing so, we obtain a more robust super-class graph that further improves thelong-tailed recognition performance. The consistent state-of-the-artexperiments on the long-tailed CIFAR-100, ImageNet, Places and iNaturalistdemonstrate the benefit of the discovered super-class graph for dealing withlong-tailed distributions.

SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail