  The long-tailed recognition (LTR) is the task of learning high-performance
classifiers given extremely imbalanced training samples between categories.
Most of the existing works address the problem by either enhancing the features
of tail classes or re-balancing the classifiers to reduce the inductive bias.
In this paper, we try to look into the root cause of the LTR task, i.e.,
training samples for each class are greatly imbalanced, and propose a
straightforward solution. We split the categories into three groups, i.e.,
many, medium and few, according to the number of training images. The three
groups of categories are separately predicted to reduce the difficulty for
classification. This idea naturally arises a new problem of how to assign a
given sample to the right class groups? We introduce a mutual exclusive
modulator which can estimate the probability of an image belonging to each
group. Particularly, the modulator consists of a light-weight module and
learned with a mutual exclusive objective. Hence, the output probabilities of
the modulator encode the data volume clues of the training dataset. They are
further utilized as prior information to guide the prediction of the
classifier. We conduct extensive experiments on multiple datasets, e.g.,
ImageNet-LT, Place-LT and iNaturalist 2018 to evaluate the proposed approach.
Our method achieves competitive performance compared to the state-of-the-art
benchmarks.
