  Long-tail recognition tackles the natural non-uniformly distributed data in
real-world scenarios. While modern classifiers perform well on populated
classes, its performance degrades significantly on tail classes. Humans,
however, are less affected by this since, when confronted with uncertain
examples, they simply opt to provide coarser predictions. Motivated by this, a
deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to
the long-tail problem, combining realism with hierarchical predictions. The
model has the option to reject classifying samples at different levels of the
taxonomy, once it cannot guarantee the desired performance. Deep-RTC is
implemented with a stochastic tree sampling during training to simulate all
possible classification conditions at finer or coarser levels and a rejection
mechanism at inference time. Experiments on the long-tailed version of four
datasets, CIFAR100, AWA2, Imagenet, and iNaturalist, demonstrate that the
proposed approach preserves more information on all classes with different
popularity levels. Deep-RTC also outperforms the state-of-the-art methods in
longtailed recognition, hierarchical classification, and learning with
rejection literature using the proposed correctly predicted bits (CPB) metric.
