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 …

Cross-modal hashing (CMH) has gained much attention due to its effectiveness and
efficiency in facilitating efficient retrieval between different modalities. Whereas, most …

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

The focus of this survey is on the analysis of two modalities of multimodal deep learning:
image and text. Unlike classic reviews of deep learning where monomodal image classifiers …

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

Deep hashing has been widely used for large-scale cross-modal retrieval benefited from the
low storage cost and fast search speed. However, most existing deep supervised methods …

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 dramatic increase in the amount of multimedia data, cross-modal similarity retrieval
has become one of the most popular yet challenging problems. Hashing offers a promising …

Hashing methods have captured much attention for cross-modal retrieval in recent years.
Most existing approaches mainly focus on preserving the semantic similarity across …

In recent years, many achievements have been made in improving the performance of
supervised cross-modal hashing. However, it remains an open issue on how to fully explore …