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 …

Large-scale data from real-world usually follow a long-tailed distribution (ie, a few majority
classes occupy plentiful training data, while most minority classes have few samples) …

Semantic segmentation usually suffers from a long tail data distribution. Due to the
imbalanced number of samples across categories, the features of those tail classes may get …

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 …

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep
neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task …

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG)
methods, the current debiasing literature mainly focuses on the long-tailed distribution …

Data privacy and long-tailed distribution are the norms rather than the exception in many
real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in …

Deep neural networks have made huge progress in the last few decades. However, as the
real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be …

This paper presents an investigation into long-tail video recognition. We demonstrate that,
unlike naturally-collected video datasets and existing long-tail image benchmarks, current …

Long-tailed distributions frequently emerge in real-world data, where a large number of
minority categories contain a limited number of samples. Such imbalance issue …