This paper presents an investigation into long-tail video recognition. We demonstrate that,
unlike naturally-collected video datasets and existing long-tail image benchmarks, current …

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses
massive volumes of unlabeled data to train neural networks. SSL techniques have evolved …

Long-tailed distributions frequently emerge in real-world data, where a large number of
minority categories contain a limited number of samples. Such imbalance issue …

Long-tailed data distributions have been a major challenge for the practical application of
deep learning. Information augmentation intends to expand the long-tailed data into uniform …

Imbalance learning is a subfield of machine learning that focuses on learning tasks in the
presence of class imbalance. Nearly all existing studies refer to class imbalance as a …

Open-set image recognition (OSR) aims to both classify known-class samples and identify
unknown-class samples in the testing set, which supports robust classifiers in many realistic …

It is usually supposed that training database is manually balanced in traditional visual
recognition tasks. However, in nature, data tends to follow long-tailed distributions. In recent …

Learning from data with long-tailed and open-ended distributions is highly challenging. In
this work, we propose OLPR, which is a new dual-stream Open-set Long-tailed recognition …

Deep learning approaches have been gaining importance in several applications. However,
the widespread use of these methods in safety-critical domains, such as Autonomous …

The long-tailed distribution is a common phenomenon in the real world. Extracted large
scale image datasets inevitably demonstrate the long-tailed property and models trained …