Unsupervised hashing methods have attracted widespread attention with the explosive
growth of large-scale data, which can greatly reduce storage and computation by learning …

Recent years have witnessed the wide application of hashing for large-scale image retrieval,
because of its high computation efficiency and low storage cost. Particularly, benefiting from …

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 …

Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task
that involves searching natural images through the use of free-hand sketches under the zero …

Multi-modal hashing makes an important contribution to multimedia retrieval, where a key
challenge is to encode heterogeneous modalities into compact hash codes. To solve this …

Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due
to its efficiency in computation and storage. In this article, we propose a novel deep semantic …

Hashing has attracted increasing research attention in recent years due to its high efficiency
of computation and storage in image retrieval. Recent works have demonstrated the …

Cross-modal hashing aims at using modality content to retrieve semantically relevant
objects of different modalities, so cross-modal retrieval has attracted much attention. To …

Supervised deep hashing aims to learn hash functions using label information. Existing
methods learn hash functions by employing either pairwise/triplet loss to explore the point-to …

Hashing technology has been widely used in approximate nearest neighbor search
algorithms for large-scale cross-modal retrieval due to its significantly reduced storage and …