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 …

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 …

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 …

Data imbalance exists ubiquitously in real-world visual regressions, eg, age estimation and
pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced …

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 …

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 …

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 …

Contrastive self-supervised learning has become a prominent technique in representation
learning. The main step in these methods is to contrast semantically similar and dissimilar …

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 …

Precise crop mapping is crucial for guiding agricultural production, forecasting crop yield,
and ensuring food security. Integrating optical and synthetic aperture radar (SAR) satellite …