Existing long-tailed classification (LT) methods only focus on tackling the class-wise
imbalance that head classes have more samples than tail classes, but overlook the attribute …

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 …

Although deep neural networks achieve tremendous success on various classification tasks,
the generalization ability drops sheer when training datasets exhibit long-tailed distributions …

Real-world data is often unbalanced and long-tailed, but deep models struggle to recognize
rare classes in the presence of frequent classes. To address unbalanced data, most studies …

The networks trained on the long-tailed dataset vary remarkably, despite the same training
settings, which shows the great uncertainty in long-tailed learning. To alleviate the …

Unlike the case when using a balanced training dataset, the per-class recall (ie, accuracy) of
neural networks trained with an imbalanced dataset are known to vary a lot from category to …

Real-world data typically follow a long-tailed distribution, where a few majority categories
occupy most of the data while most minority categories contain a limited number of samples …

To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

Despite the success of the deep neural networks, it remains challenging to effectively build a
system for long-tail visual recognition tasks. To address this problem, we first investigate the …

In this paper, our goal is to design a simple learning paradigm for long-tail visual
recognition, which not only improves the robustness of the feature extractor but also …