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 …

In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …

In the real open world, data tends to follow long-tailed class distributions, motivating the well-
studied long-tailed recognition (LTR) problem. Naive training produces models that are …

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 …

Real-world datasets are typically imbalanced in the sense that only a few classes have
numerous samples, while many classes are associated with only a few samples. As a result …

Recently, computer vision foundation models such as CLIP and ALI-GN, have shown
impressive generalization capabilities on various downstream tasks. But their abilities to …

This paper studies the problem of object discovery--separating objects from the background
without manual labels. Existing approaches utilize appearance cues, such as color, texture …

Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-
by-batch manner during inference. While label distributions often exhibit imbalances in real …

Class imbalance is a common challenge in real-world recognition tasks, where the majority
of classes have few samples, also known as tail classes. We address this challenge with the …