Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

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 …

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 …

As the class size grows, maintaining a balanced dataset across many classes is challenging
because the data are long-tailed in nature; it is even impossible when the sample-of-interest …

Despite the success of the deep neural networks, it remains challenging to effectively build a
system for long-tail visual recognition tasks. To address this problem, we first investigate the …

Deep classifiers have achieved great success in visual recognition. However, real-world
data is long-tailed by nature, leading to the mismatch between training and testing …

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 …

Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …