Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various
tasks, but we cannot accurately know the importance of different design dimensions of …

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 …

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

Unsupervised video hashing usually optimizes binary codes by learning to reconstruct input
videos. Such reconstruction constraint spends much effort on frame-level temporal context …

Recent pretrained vision-language models have achieved impressive performance on cross-
modal retrieval tasks in English. Their success, however, heavily depends on the availability …

The rise in loosely-structured data available through text, images, and other modalities has
called for new ways of querying them. Multimedia Information Retrieval has filled this gap …

Given a text/image query, image-text retrieval aims to find the relevant items in the database.
Recently, visual-linguistic pre-training (VLP) methods have demonstrated promising …

Deep cross-modal hash retrieval (DCMHR) methods can effectively analyze the correlation
of multimodal data while maintaining efficiency. However, to pursue better accuracy, most …

In the multimedia era, image becomes an effective medium in search advertising. Dynamic
Image Advertising (DIA), a system that matches queries with appropriate ad images and …

By exploiting the cross-modal attention, cross-BERT methods have achieved state-of-the-art
accuracy in cross-modal retrieval. Nevertheless, the heavy text-image interactions in the …