Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval
becomes popular with a burst of 2D and 3D data. However, this problem is challenging …

There has been significant attention devoted to the effectiveness of various domains, such
as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the …

Supervised cross-modal hashing has received wide attention in recent years. However,
existing methods primarily rely on sample-wise semantic relationships to evaluate the …

Large-scale datasets have played a crucial role in the advancement of computer vision.
However, they often suffer from problems such as class imbalance, noisy labels, dataset …

Deep hashing has been widely used in multimedia retrieval systems due to its storage and
computation efficiency. Unsupervised hashing has received a lot of attention in recent years …

Recently, numerous proxy hash code based methods, which sufficiently exploit the label
information of data to supervise the training of hashing models, have been proposed …

Abstract Convolutional Neural Networks (CNNs) are powerful in learning patterns of different
vision tasks, but they are sensitive to label noise and may overfit to noisy labels during …

Learning with noisy labels has gained increasing attention because the inevitable imperfect
labels in real-world scenarios can substantially hurt the deep model performance. Recent …

Cross-modal hashing retrieval approaches have received extensive attention owing to their
storage superiority and retrieval efficiency. To achieve better retrieval performances …

The surge in multi-modal data has propelled cross-modal matching to the forefront of
research interest. However the challenge lies in the laborious and expensive process of …