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.

本文提出了一种新的端到端的深层交叉模态哈希方法 (DCHUC)，通过迭代优化算法学习图像 - 文本对的统一哈希码和一对哈希函数，并使用所学的统一哈希码来指导哈希函数的学习，同时学习到的哈希函数也可以反馈指导统一哈希码的优化过程。实验结果表明，该方法优于现有的交叉模态哈希方法。