Cross-modal hashing has been widely used in multimedia retrieval tasks due to its fast
retrieval speed and low storage cost. Recently, many deep unsupervised cross-modal …

Cross-modal hashing that leverages hash functions to project high-dimensional data from
different modalities into the compact common hamming space, has shown immeasurable …

Vision-language pre-trained (VLP) models have been the foundation of numerous vision-
language tasks. Given their prevalence, it becomes imperative to assess their adversarial …

The similarity calculation is too simple in most cross-modal hash retrieval methods, which do
not consider the impact of the relations between instances. To solve this problem, this paper …

Deep cross-modal retrieval techniques have recently achieved remarkable performance,
which also poses severe threats to data privacy potentially. Nowadays, enormous user …

Existing cross-modal hash retrieval methods can simultaneously enhance retrieval speed
and reduce storage space. However, these methods face a major challenge in determining …

Cross-modal hashing is an important approach for multimodal data management and
application. Existing unsupervised cross-modal hashing algorithms mainly rely on data …

Cross-modal hashing has gained considerable attention in cross-modal retrieval due to its
low storage cost and prominent computational efficiency. However, preserving more …

With the ever-increasing multimedia data on the Web, cross-modal video-text retrieval has
received a lot of attention in recent years. Deep cross-modal hashing approaches utilize the …

Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information to
recognize seen and unseen samples, where unseen classes are not observable during …