As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

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 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) …

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 …

Learning discriminative image representations plays a vital role in long-tailed image
classification because it can ease the classifier learning in imbalanced cases. Given the …

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been
under studied. While existing semi-supervised learning (SSL) methods are known to perform …

The problem of class imbalanced data is that the generalization performance of the classifier
deteriorates due to the lack of data from minority classes. In this paper, we propose a novel …

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 …

The current evaluation protocol of long-tailed visual recognition trains the classification
model on the long-tailed source label distribution and evaluates its performance on 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 …