The visual world naturally exhibits a long-tailed distribution of open classes, which poses
great challenges to modern visual systems. Existing approaches either perform class re …

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 …

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 …

It's widely acknowledged that deep learning models with flatter minima in its loss landscape
tend to generalize better. However, such property is under-explored in deep long-tailed …

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 …

In the real open world, data tends to follow long-tailed class distributions, motivating the well-
studied long-tailed recognition (LTR) problem. Naive training produces models that are …

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

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 …

Deep neural networks excel in visual recognition tasks, but their success hinges on access
to balanced datasets. Yet, real-world datasets often exhibit a long-tailed distribution …

Long-tailed recognition with imbalanced class distribution naturally emerges in practical
machine learning applications. Existing methods such as data reweighing, resampling, and …