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 …

Class imbalance problems frequently occur in real-world tasks, and conventional deep
learning algorithms are well known for performance degradation on imbalanced training …

Many real-world recognition problems are characterized by long-tailed label distributions.
These distributions make representation learning highly challenging due to limited …

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 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 …

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 …

Deep neural networks may perform poorly when training datasets are heavily class-
imbalanced. Recently, two-stage methods decouple representation learning and classifier …

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 …

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