In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

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 …

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 …

One-stage long-tailed recognition methods improve the overall performance in a" seesaw"
manner, ie, either sacrifice the head's accuracy for better tail classification or elevate the …

Existing long-tailed recognition methods, aiming to train class-balanced models from long-
tailed data, generally assume the models would be evaluated on the uniform test class …

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 …

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 …

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 …

Federated learning (FL) provides a privacy-preserving solution for distributed machine
learning tasks. One challenging problem that severely damages the performance of FL …