摘要
由于现有的大多数跨模态哈希方法未能有效地探究不同模态数据之间的相关性以及多样性,导致检索性能不尽如人意。为了克服该问题,提出一种简单而有效的深度跨模态哈希方法——深度共性保持哈希,可以在简单的端到端网络中同时学到模态共享表示和模态私有表示,并生成对应模态的判别性紧凑哈希码。与现有的基于深度的跨模态哈希方法相比,所提出的方法的模型复杂度和计算量几乎可以忽略不计,但是获得了显著的性能提升。在三个跨模态数据集上的大量实验结果表明,该方法优于其他当前最先进的跨模态哈希方法。
At present,most existing cross-modal hashing methods fail to explore the relevance and diversity of different modality data,thus leading to unsatisfactory search performance.In order to solve the above problem,a simple yet efficient deep hashing model is proposed,named deep consistency-preserving hashing for cross-modal retrieval that simultaneously exploits modality-common representation and modality-private representation through the simple end-to-end network structure,and generates compact and discriminative hash codes for multiple modalities.Compared with other deep cross-modal hashing methods,the complexity and computation of the proposed method can be neglected with significant performance improvements.Comprehensive evaluations are conducted on three cross-modal benchmark datasets which illustrate that the proposed method is superior to the state-of-the-art cross-modal hashing methods.
作者
石娟
谢德
蒋庆
SHI Juan;XIE De;JIANG Qing(School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China;School of Electronic Engineering,Xidian University,Xi’an 710071,China;Dept of Information Construction and Management,Baise Executive Leadership Academy,Baise 533013,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2021年第3期71-77,98,共8页
Journal of Xidian University
基金
广西重点研发计划(桂科AB18126094)。
关键词
多模态学习
哈希
模态共享表示
模态私有表示
检索
multi-modal learning
hash
modality-common representation
modality-private representation
retrieval