# Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning

arXiv: https://arxiv.org/abs/1907.12490 · HTML: https://mlsanity.com/paper/1907.12490/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Unsupervised Hashing with Semantic Concept Mining](https://mlsanity.com/paper/2209.11475/) (2022)
- [Deep Self-Adaptive Hashing for Image Retrieval](https://mlsanity.com/paper/2108.07094/) (2021)
- [PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval](https://mlsanity.com/paper/2503.16064/) (2025)
- [Deep Unsupervised Hashing with Latent Semantic Components](https://mlsanity.com/paper/2203.09420/) (2022)
- [FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysis](https://mlsanity.com/paper/2407.20114/) (2024)
- [Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval](https://mlsanity.com/paper/2502.19751/) (2025)

## Abstract (arXiv)

Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow cross-modal hashing methods, deep cross-modal hashing methods can achieve a more satisfactory performance by integrating feature learning and hash codes optimizing into a same framework. However, most existing deep cross-modal hashing methods either cannot learn a unified hash code for the two correlated data-points of different modalities in a database instance or cannot guide the learning of unified hash codes by the feedback of hashing function learning procedure, to enhance the retrieval accuracy. To address the issues above, in this paper, we propose a novel end-to-end Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning (DCHUC). Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs. With the iterative optimization algorithm, the learned unified hash codes can be used to guide the hashing function learning procedure; Meanwhile, the learned hashing functions can feedback to guide the unified hash codes optimizing procedure. Extensive experiments on three public datasets demonstrate that the proposed method outperforms the state-of-the-art cross-modal hashing methods.