To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

The task of dynamic scene graph generation (SGG) from videos is complicated and
challenging due to the inherent dynamics of a scene, temporal fluctuation of model …

Scene text recognition is a popular research topic which is also extensively utilized in the
industry. Although many methods have achieved satisfactory performance for the close-set …

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep
neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task …

Graph classification is a crucial task in many real-world multimedia applications, where
graphs can represent various multimedia data types such as images, videos, and social …

Traffic sign recognition is a crucial aspect of autonomous vehicle research, and deep
learning techniques have significantly contributed to its progress. Nevertheless, the …

Modern image classifiers perform well on populated classes while degrading considerably
on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long …

In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited
due to the under-representation of tail samples. Class rebalancing, information …

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 …

Text-to-image generation has achieved astonishing results yet precise spatial controllability
and prompt fidelity remain highly challenging. This limitation is typically addressed through …