# Discrete Latent Factor Model for Cross-Modal Hashing

arXiv: https://arxiv.org/abs/1707.08322 · HTML: https://mlsanity.com/paper/1707.08322/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing](https://mlsanity.com/paper/2112.06489/) (2021)
- [FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval](https://mlsanity.com/paper/2105.07128/) (2021)
- [MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval](https://mlsanity.com/paper/2109.00883/) (2021)
- [Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data](https://mlsanity.com/paper/2109.04260/) (2021)
- [Differentially Private Motif-Preserving Multi-modal Hashing](https://mlsanity.com/paper/2605.15460/) (2026)

## Abstract (arXiv)

Due to its storage and retrieval efficiency, cross-modal hashing~(CMH) has been widely used for cross-modal similarity search in multimedia applications. According to the training strategy, existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than relaxation-based continuous methods, but the training of discrete methods is time-consuming. In this paper, we propose a novel CMH method, called discrete latent factor model based cross-modal hashing~(DLFH), for cross modal similarity search. DLFH is a discrete method which can directly learn the binary hash codes for CMH. At the same time, the training of DLFH is efficient. Experiments on real datasets show that DLFH can achieve significantly better accuracy than existing methods, and the training time of DLFH is comparable to that of relaxation-based continuous methods which are much faster than existing discrete methods.