Our work focuses on tackling the challenging but natural visual recognition task of long-
tailed data distribution (ie, a few classes occupy most of the data, while most classes have …

Long-tail recognition tackles the natural non-uniformly distributed data in real-world
scenarios. While modern classifiers perform well on populated classes, its performance …

Recent success in deep learning has partially been driven by training increasingly
overparametrized networks on ever larger datasets. It is therefore natural to ask: how much …

Real world data often have a long-tailed and open-ended distribution. A practical
recognition system must classify among majority and minority classes, generalize from a few …

Neural networks trained with class-imbalanced data are known to perform poorly on minor
classes of scarce training data. Several recent works attribute this to over-fitting to minor …

Class imbalance is a common challenge in real-world recognition tasks, where the majority
of classes have few samples, also known as tail classes. We address this challenge with the …

The problem of long-tailed recognition, where the number of examples per class is highly
unbalanced, is considered. It is hypothesized that the well known tendency of standard …

Effective training of the deep neural networks requires much data to avoid underdetermined
and poor generalization. Data Augmentation alleviates this by using existing data more …

Real-world data often exhibits class-imbalanced distributions, where a few classes (aka
majority classes) occupy most instances and lots of classes (aka minority classes) have few …

In class incremental learning (CIL) a model must learn new classes in a sequential manner
without forgetting old ones. However, conventional CIL methods consider a balanced …