Face recognition is one of the most active research fields of computer vision and pattern
recognition, with many practical and commercial applications including identification, access …

Face recognition technology is a biometric technology, which is based on the identification
of facial features of a person. People collect the face images, and the recognition equipment …

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 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 …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

Real-world data often exhibit imbalanced distributions, where certain target values have
significantly fewer observations. Existing techniques for dealing with imbalanced data focus …

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 …

Graph-based fraud detection approaches have escalated lots of attention recently due to the
abundant relational information of graph-structured data, which may be beneficial for the …

The long-tail distribution of the visual world poses great challenges for deep learning based
classification models on how to handle the class imbalance problem. Existing solutions …

Object recognition techniques using convolutional neural networks (CNN) have achieved
great success. However, state-of-the-art object detection methods still perform poorly on …