With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval
methods struggle to meet the needs of users seeking access to data across various …

With the explosive growth of multimedia contents, multimedia retrieval is facing
unprecedented challenges on both storage cost and retrieval speed. Hashing technique can …

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential
recommender systems by achieving state-of-the-art recommendation performance on …

Unsupervised cross-modal hashing has attracted considerable attention to support large-
scale cross-modal retrieval. Although promising progresses have been made so far, existing …

Recent days witness significant progress in various multi-modal tasks made by Contrastive
Language-Image Pre-training (CLIP), a multi-modal large-scale model that learns visual …

With the explosive growth of multi-modal information on the Internet, the multi-modal
knowledge graph (MMKG) has become an important research topic in knowledge graphs to …

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 …

In recent years, Cross-Modal Hashing (CMH) has attracted much attention due to its fast
query speed and efficient storage. Previous studies have achieved promising results for …

Cross-modal hash has become a key technology for large datasets retrieval. However, some
challenges still need to be tackled: 1) How to effectively embed semantic information into …

Deep cross-modal hashing retrieval models inherit the vulnerability of deep neural networks.
They are vulnerable to adversarial attacks, especially for the form of subtle perturbations to …