Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Long-tailed image recognition presents massive challenges to deep learning systems since
the imbalance between majority (head) classes and minority (tail) classes severely skews …

Deep learning algorithms face great challenges with long-tailed data distribution which,
however, is quite a common case in real-world scenarios. Previous methods tackle the …

The long-tail distribution of the visual world poses great challenges for deep learning based
classification models on how to handle the class imbalance problem. Existing solutions …

The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …

Deep learning has achieved remarkable progress for visual recognition on large-scale
balanced datasets but still performs poorly on real-world long-tailed data. Previous methods …

Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

In recent years, visual recognition on challenging long-tailed distributions, where classes
often exhibit extremely imbalanced frequencies, has made great progress mostly based on …

In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …

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 …